Knowledge based reasoning using aiml.pdf

Knowledge and Reasoning
Table of Contents
• Knowledge and reasoning-Approaches and issues of knowledge reasoning-Knowledge
base agents
• Logic Basics-Logic-Propositional logic-syntax ,semantics and inferences-Propositional
logic- Reasoning patterns
• Unification and Resolution-Knowledge representation using rules-Knowledge
representation using semantic nets
• Knowledge representation using frames-Inferences-
• Uncertain Knowledge and reasoning-Methods-Bayesian probability and belief network
• Probabilistic reasoning-Probabilistic reasoning over time-Probabilistic reasoning over time
• Other uncertain techniques-Data mining-Fuzzy logic-Dempster -shafer theory
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Knowledge Representation & Reasoning
• The second most important concept in AI
• If we are going to act rationally in our environment, then we must have some way of
describing that environment and drawing inferences from that representation.
• how do we describe what we know about the world ?
• how do we describe it concisely ?
• how do we describe it so that we can get hold of the right piece of knowledge when
we need it ?
• how do we generate new pieces of knowledge ?
• how do we deal with uncertain knowledge ?

Knowledge
Declarative Procedural
• Declarative knowledge deals with factoid questions (what is the capital of
India? Etc.)
• Procedural knowledge deals with “How”
• Procedural knowledge can be embedded in declarative knowledge
Knowledge Representation & Reasoning

Planning
Given a set of goals, construct a sequence of actions that achieves
those goals:
• often very large search space
• but most parts of the world are independent of most other
parts
• often start with goals and connect them to actions
• no necessary connection between order of planning and order
of execution
• what happens if the world changes as we execute the plan
and/or our actions don’t produce the expected results?

Learning
•If a system is going to act truly appropriately, then it must
be able to change its actions in the light of experience:
•how do we generate new facts from old ?
•how do we generate new concepts ?
•how do we learn to distinguish different situations in
new environments ?

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What is knowledge representation?
•Knowledge representation and reasoning (KR, KRR) is the part of Artificial
intelligence which concerned with AI agents thinking and how thinking contributes
to intelligent behavior of agents.
•It is responsible for representing information about the real world so that a
computer can understand and can utilize this knowledge to solve the complex real
world problems such as diagnosis a medical condition or communicating with
humans in natural language.
•It is also a way which describes how we can represent knowledge in artificial
intelligence. Knowledge representation is not just storing data into some database,
but it also enables an intelligent machine to learn from that knowledge and
experiences so that it can behave intelligently like a human.

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What to Represent?
Following are the kind of knowledge which needs to be represented in AI systems:
•Object: All the facts about objects in our world domain. E.g., Guitars contains
strings, trumpets are brass instruments.
•Events: Events are the actions which occur in our world.
•Performance: It describe behavior which involves knowledge about how to do
things.
•Meta-knowledge: It is knowledge about what we know.
•Facts: Facts are the truths about the real world and what we represent.
•Knowledge-Base: The central component of the knowledge-based agents is the
knowledge base. It is represented as KB. The Knowledgebase is a group of the
Sentences (Here, sentences are used as a technical term and not identical with the
English language).

Approaches to knowledge Representation
• Representational adequacy the ability to represent all of the kinds of
knowledge that are needed in that domain.
• Inferential Adequacy: - the ability to manipulate the representation structures
in such a way as to derive new structures corresponding to new knowledge
inferred from ol.
• Inferential Efficiency: - the ability to incorporate into the knowledge structure
additional information that can be used to focus the attention of the inference
mechanism in the most promising directions.
• Acquisitioned Efficiency: - the ability to acquire new information easily. The
simplest case involves direct insertion by a person of new knowledge into the
database.

Knowledge Representation Issues
• It becomes clear that particular knowledge representation models allow for more specific more powerful
problem solving mechanisms that operate on them.
• Examine specific techniques that can be used for representing & manipulating knowledge within programs.
• Representation & Mapping
• Facts :- truths in some relevant world
• These are the things we want to represent.
• Representations of facts in some chosen formalism.
• Things we are actually manipulating. Structuring these entities is as two levels.
• The knowledge level, at which facts concluding each agents behavior & current goals are described.
Internal Representations
Facts
English Representations
English understanding English generation

Table of Contents
• Knowledge and reasoning-Approaches and issues of knowledge reasoning-Knowledge
base agents
• Logic Basics-Logic-Propositional logic-syntax ,semantics and inferences-Propositional
logic- Reasoning patterns
• Probabilistic reasoning-Probabilistic reasoning over time-Probabilistic reasoning over
time
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A KNOWLEDGE-BASED AGENT
12
• A knowledge-based agent includes a knowledge base and an inference system.
• A knowledge base is a set of representations of facts of the world.
• Each individual representation is called a sentence.
• The sentences are expressed in a knowledge representation language.
• The agent operates as follows:
1. It TELLs the knowledge base what it perceives.
2. It ASKs the knowledge base what action it should perform.
3. It performs the chosen action.
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Requirements for a Knowledge-Based Agent
1. what it already knows" [McCarthy '59]
A knowledge base of beliefs.
2. it must rst be capable of being told" [McCarthy '59]
A way to put new beliefs into the knowledge base.
3. automatically deduces for itself a suciently wide class of
immediate consequences" [McCarthy '59]
A reasoning mechanism to derive new beliefs from ones already
in the knowledge base.
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ARCHITECTURE OF A KNOWLEDGE-BASED
AGENT
14
• Knowledge Level.
• The most abstract level: describe agent by saying what it knows.
• Example: A taxi agent might know that the Golden Gate Bridge connects San
Francisco with the Marin County.
• Logical Level.
• The level at which the knowledge is encoded into sentences.
• Example: Links(GoldenGateBridge, SanFrancisco, MarinCounty).
• Implementation Level.
• The physical representation of the sentences in the logical level.
• Example: ‘(links goldengatebridge sanfrancisco marincounty)
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THE WUMPUS WORLD ENVIRONMENT
15
• The Wumpus computer game
• The agent explores a cave consisting of rooms connected by passageways.
• Lurking somewhere in the cave is the Wumpus, a beast that eats any agent that
enters its room.
• Some rooms contain bottomless pits that trap any agent that wanders into the
room.
• Occasionally, there is a heap of gold in a room.
• The goal is to collect the gold and exit the world without being eaten
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A TYPICAL WUMPUS WORLD
16
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• The agent always starts in the
field [1,1].
• The task of the agent is to
find the gold, return to the
field [1,1] and climb out of
the cave.

AGENT IN A WUMPUS WORLD: PERCEPTS
17
• The agent perceives
• a stench in the square containing the Wumpus and in the adjacent squares (not
diagonally)
• a breeze in the squares adjacent to a pit
• a glitter in the square where the gold is
• a bump, if it walks into a wall
• a woeful scream everywhere in the cave, if the wumpus is killed
• The percepts are given as a five-symbol list. If there is a stench and a breeze, but no
glitter, no bump, and no scream, the percept is
[Stench, Breeze, None, None, None]
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WUMPUS WORLD ACTIONS
18
• go forward
• turn right 90 degrees
• turn left 90 degrees
• grab: Pick up an object that is in the same square as the agent
• shoot: Fire an arrow in a straight line in the direction the agent is facing. The arrow
continues until it either hits and kills the wumpus or hits the outer wall. The agent
has only one arrow, so only the first Shoot action has any effect
• climb is used to leave the cave. This action is only effective in the start square
• die: This action automatically and irretrievably happens if the agent enters a square
with a pit or a live wumpus
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ILLUSTRATIVE EXAMPLE: WUMPUS WORLD
•Performance measure
• gold +1000,
• death -1000
(falling into a pit or being eaten by the wumpus)
• -1 per step, -10 for using the arrow
•Environment
• Rooms / squares connected by doors.
• Squares adjacent to wumpus are smelly
• Squares adjacent to pit are breezy
• Glitter iff gold is in the same square
• Shooting kills wumpus if you are facing it
• Shooting uses up the only arrow
• Grabbing picks up gold if in same square
• Releasing drops the gold in same square
• Randomly generated at start of game. Wumpus only senses current room.
•Sensors: Stench, Breeze, Glitter, Bump, Scream [perceptual inputs]
•Actuators: Left turn, Right turn, Forward, Grab, Release, Shoot
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WUMPUS WORLD CHARACTERIZATION
Fully Observable No – only local perception
Deterministic Yes – outcomes exactly specified
Static Yes – Wumpus and Pits do not move
Discrete Yes
Single-agent? Yes – Wumpus is essentially a “natural feature.”
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EXPLORING A WUMPUS WORLD
Stench, Breeze, Glitter, Bump, Scream
None, none, none, none, none
The knowledge base of the agent
consists of the rules of the
Wumpus world plus the percept
“nothing” in [1,1]
Boolean percept
feature values:
<0, 0, 0, 0, 0>
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None, none, none, none, none
T=0 The KB of the agent consists of
the rules of the Wumpus world plus
the percept “nothing” in [1,1].
By inference, the agent’s knowledge
base also has the information that
[2,1] and [1,2] are okay.
Added as propositions.
World “known” to agent
at time = 0.
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@ T = 1 What follows?
Pit(2,2) or Pit(3,1)
None, none, none, none, none A – agent
V – visited
B - breeze
None, breeze, none, none, none
Where next?
T = 0
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V A/B P?
P?
T = 1

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S
Where is Wumpus? Wumpus cannot be in (1,1) or in (2,2) (Why?)➔ Wumpus in (1,3)
Not breeze in (1,2) ➔ no pit in (2,2); but we know there is
pit in (2,2) or (3,1) ➔ pit in (3,1)
P?
P?
1 2 3 4
1
2
3
4
Stench, none, none, none, none
P
W
P
S
T=3

P
W
P
We reasoned about the possible states the Wumpus world can be in,
given our percepts and our knowledge of the rules of the Wumpus
world.
I.e., the content of KB at T=3.
Essence of logical reasoning:
Given all we know, Pit_in_(3,1) holds.
(“The world cannot be different.”)
What follows is what holds true in all those worlds that satisfy what is
known at that time T=3 about the particular Wumpus world we are in.
Models(KB) Models(P_in_(3,1))
Example property: P_in_(3,1)
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NO INDEPENDENT ACCESS TO THE WORLD
26
• The reasoning agent often gets its knowledge about the facts of the world as a sequence of
logical sentences and must draw conclusions only from them without independent access to
the world.
• Thus it is very important that the agent’s reasoning is sound!
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SUMMARY OF KNOWLEDGE BASED AGENTS
27
• Intelligent agents need knowledge about the world for making good decisions.
• The knowledge of an agent is stored in a knowledge base in the form of sentences in a
knowledge representation language.
• A knowledge-based agent needs a knowledge base and an inference mechanism. It
operates by storing sentences in its knowledge base, inferring new sentences with the
inference mechanism, and using them to deduce which actions to take.
• A representation language is defined by its syntax and semantics, which specify the
structure of sentences and how they relate to the facts of the world.
• The interpretation of a sentence is the fact to which it refers. If this fact is part of the
actual world, then the sentence is true.
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Table of Contents
• Knowledge and reasoning-Approaches and issues of knowledge reasoning-Knowledge base
agents
• Logic Basics-Logic-Propositional logic-syntax ,semantics and inferences-Propositional logic-
Reasoning patterns
• Unification and Resolution-Knowledge representation using rules-Knowledge representation
using semantic nets
• Probabilistic reasoning-Probabilistic reasoning over time-Probabilistic reasoning over time
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What is a Logic?
• A language with concrete rules
• No ambiguity in representation (may be other errors!)
• Allows unambiguous communication and processing
• Very unlike natural languages e.g. English
• Many ways to translate between languages
• A statement can be represented in different logics
• And perhaps differently in same logic
• Expressiveness of a logic
• How much can we say in this language?
• Not to be confused with logical reasoning
• Logics are languages, reasoning is a process (may use logic)
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Syntax and Semantics
• Syntax
• Rules for constructing legal sentences in the logic
• Which symbols we can use (English: letters, punctuation)
• How we are allowed to combine symbols
• Semantics
• How we interpret (read) sentences in the logic
• Assigns a meaning to each sentence
• Example: “All lecturers are seven foot tall”
• A valid sentence (syntax)
• And we can understand the meaning (semantics)
• This sentence happens to be false (there is a counterexample)

Propositional Logic
• Syntax
• Propositions, e.g. “it is wet”
• Connectives: and, or, not, implies, iff (equivalent)
• Brackets, T (true) and F (false)
• Semantics (Classical AKA Boolean)
• Define how connectives affect truth
• “P and Q” is true if and only if P is true and Q is true
• Use truth tables to work out the truth of statements

Predicate Logic
• Propositional logic combines atoms
• An atom contains no propositional connectives
• Have no structure (today_is_wet, john_likes_apples)
• Predicates allow us to talk about objects
• Properties: is_wet(today)
• Relations: likes(john, apples)
• True or false
• In predicate logic each atom is a predicate
• e.g. first order logic, higher-order logic

First Order Logic
• More expressive logic than propositional
• Used in this course (Lecture 6 on representation in FOL)
• Constants are objects: john, apples
• Predicates are properties and relations:
• likes(john, apples)
• Functions transform objects:
• likes(john, fruit_of(apple_tree))
• Variables represent any object: likes(X, apples)
• Quantifiers qualify values of variables
• True for all objects (Universal): X. likes(X, apples)
• Exists at least one object (Existential): X. likes(X, apples)

Example: FOL Sentence
• “Every rose has a thorn”
• For all X
• if (X is a rose)
• then there exists Y
• (X has Y) and (Y is a thorn)

Example: FOL Sentence
• “On Mondays and Wednesdays I go to John’s house for dinner”
⚫ Note the change from “and” to “or”
– Translating is problematic

Higher Order Logic
• More expressive than first order
• Functions and predicates are also objects
• Described by predicates: binary(addition)
• Transformed by functions: differentiate(square)
• Can quantify over both
• E.g. define red functions as having zero at 17
• Much harder to reason with

Beyond True and False
• Multi-valued logics
• More than two truth values
• e.g., true, false & unknown
• Fuzzy logic uses probabilities, truth value in [0,1]
• Modal logics
• Modal operators define mode for propositions
• Epistemic logics (belief)
• e.g. p (necessarily p), p (possibly p), …
• Temporal logics (time)
• e.g. p (always p), p (eventually p), …

Table of Contents
• Knowledge and reasoning-Approaches and issues of knowledge reasoning-
Knowledge base agents
• Logic Basics-Logic-Propositional logic-syntax ,semantics and inferences-
Propositional logic- Reasoning patterns
• Uncertain Knowledge and reasoning-Methods-Bayesian probability and belief
network
• Probabilistic reasoning-Probabilistic reasoning over time-Probabilistic
reasoning over time
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Table of Contents
network
reasoning over time
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Propositional logic
• Propositional logic consists of:
• The logical values true and false (T and F)
• Propositions: “Sentences,” which
• Are atomic (that is, they must be treated as indivisible units, with
no internal structure), and
• Have a single logical value, either true or false
• Operators, both unary and binary; when applied to logical values, yield
logical values
• The usual operators are and, or, not, and implies
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Truth tables
• Logic, like arithmetic, has operators, which apply to one, two, or more
values (operands)
• A truth table lists the results for each possible arrangement of operands
• Order is important: x op y may or may not give the same result as y op x
• The rows in a truth table list all possible sequences of truth values for n
operands, and specify a result for each sequence
• Hence, there are 2n rows in a truth table for n operands
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Unary operators
X Constant true, (T)
T T
F T
• There are four possible unary operators:
• Only the last of these (negation) is widely used (and has a symbol,¬ ,for the operation
X Constant false, (F)
T F
F F
X Identity, (X)
T T
F F
X Negation, ¬X
T F
F T
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Combined tables for unary operators
X Constant T Constant F Identity ¬X
T T F T F
F T F F T

44
Binary operators
• There are sixteen possible binary operators:
• All these operators have names, but I haven’t tried to fit them in
• Only a few of these operators are normally used in logic
X Y
T T T T T T T T T T F F F F F F F F
T F T T T T F F F F T T T T F F F F
F T T T F F T T F F T T F F T T F F
F F T F T F T F T F T F T F T F T F
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Useful binary operators
• Here are the binary operators that are traditionally used:
• Notice in particular that material implication () only approximately means the same as the
English word “implies”
• All the other operators can be constructed from a combination of these (along with unary
not, ¬)
X Y
AND
X  Y
OR
X  Y
IMPLIES
X  Y
BICONDITIONAL
X  Y
T T T T T T
T F F T F F
F T F T T F
F F F F T T
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Logical expressions
• All logical expressions can be computed with some combination of and (),
or (), and not () operators
• For example, logical implication can be computed this way:
• Notice that X  Y is equivalent to X  Y
X Y X X  Y X  Y
T T F T T
T F F F F
F T T T T
F F T T T
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Another example
• Exclusive or (xor) is true if exactly one of its operands is true
• Notice that (XY)(XY) is equivalent to X xor Y
X Y X Y X  Y X  Y (XY)(XY) X xor Y
T T F F F F F F
T F F T F T T T
F T T F T F T T
F F T T F F F F
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World
• A world is a collection of prepositions and logical expressions relating those
prepositions
• Example:
• Propositions: JohnLovesMary, MaryIsFemale, MaryIsRich
• Expressions:
MaryIsFemale  MaryIsRich  JohnLovesMary
• A proposition “says something” about the world, but since it is atomic (you
can’t look inside it to see component parts), propositions tend to be very
specialized and inflexible
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Models
A model is an assignment of a truth value to each proposition, for example:
• JohnLovesMary: T, MaryIsFemale: T, MaryIsRich: F
• An expression is satisfiable if there is a model for which the expression is true
• For example, the above model satisfies the expression
• An expression is valid if it is satisfied by every model
• This expression is not valid:
because it is not satisfied by this model:
JohnLovesMary: F, MaryIsFemale: T, MaryIsRich: T
• But this expression is valid:
MaryIsFemale  MaryIsRich  MaryIsFemale
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Inference rules in propositional logic
• Here are just a few of the rules you can apply when reasoning in propositional logic:
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Implication elimination
• A particularly important rule allows you to get rid of
the implication operator,  :
•X  Y  X  Y
• We will use this later on as a necessary tool for
simplifying logical expressions
• The symbol  means “is logically equivalent to”
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Conjunction elimination
• Another important rule for simplifying logical expressions
allows you to get rid of the conjunction (and) operator,  :
• This rule simply says that if you have an and operator at the
top level of a fact (logical expression), you can break the
expression up into two separate facts:
• MaryIsFemale  MaryIsRich
• becomes:
• MaryIsFemale
• MaryIsRich
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Inference by computer
• To do inference (reasoning) by computer is basically a search process,
taking logical expressions and applying inference rules to them
• Which logical expressions to use?
• Which inference rules to apply?
• Usually you are trying to “prove” some particular statement
• Example:
• it_is_raining  it_is_sunny
• it_is_sunny  I_stay_dry
• it_is_rainy  I_take_umbrella
• I_take_umbrella  I_stay_dry
• To prove: I_stay_dry
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Table of Contents
network
reasoning over time
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Reasoning Patterns
• Inference in propositional logic is NP-complete!
• However, inference in propositional logic shows
monoticity:
•Adding more rules to a knowledge base does not
affect earlier inferences

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Forward and backward reasoning
• Situation: You have a collection of logical expressions (premises), and
you are trying to prove some additional logical expression (the
conclusion)
• You can:
• Do forward reasoning: Start applying inference rules to the logical
expressions you have, and stop if one of your results is the
conclusion you want
• Do backward reasoning: Start from the conclusion you want, and
try to choose inference rules that will get you back to the logical
expressions you have
• With the tools we have discussed so far, neither is feasible
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Example
• Given:
• it_is_raining  I_take_umbrella
• You can conclude:
• it_is_sunny  it_is_raining
• I_take_umbrella  it_is_sunny
• I_stay_dry  I_take_umbrella
• Etc., etc. ... there are just too many things you can conclude!
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Predicate calculus
• Predicate calculus is also known as “First Order Logic” (FOL)
• Predicate calculus includes:
• All of propositional logic
• Logical values true, false
• Variables x, y, a, b,...
• Connectives , , , , 
• Constants KingJohn, 2, Villanova,...
• Predicates Brother, >,...
• Functions Sqrt, MotherOf,...
• Quantifiers , 
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Constants, functions, and predicates
• A constant represents a “thing”--it has no truth value, and it
does not occur “bare” in a logical expression
• Examples: DavidMatuszek, 5, Earth, goodIdea
• Given zero or more arguments, a function produces a
constant as its value:
• Examples: motherOf(DavidMatuszek), add(2, 2),
thisPlanet()
• A predicate is like a function, but produces a truth value
• Examples: greatInstructor(DavidMatuszek),
isPlanet(Earth), greater(3, add(2, 2))
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Universal quantification
• The universal quantifier, , is read as “for each”
or “for every”
• Example: x, x2  0 (for all x, x2 is greater than or equal to zero)
• Typically,  is the main connective with :
x, at(x,Villanova)  smart(x)
means “Everyone at Villanova is smart”
• Common mistake: using  as the main connective with :
x, at(x,Villanova)  smart(x)
means “Everyone is at Villanova and everyone is smart”
• If there are no values satisfying the condition, the result is true
• Example: x, isPersonFromMars(x)  smart(x) is true
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Existential quantification
• The existential quantifier, , is read “for some” or “there exists”
• Example: x, x2 < 0 (there exists an x such that x2 is less than zero)
• Typically,  is the main connective with :
x, at(x,Villanova)  smart(x)
means “There is someone who is at Villanova and is smart”
• Common mistake: using  as the main connective with :
x, at(x,Villanova)  smart(x)
This is true if there is someone at Villanova who is smart...
...but it is also true if there is someone who is not at Villanova
By the rules of material implication, the result of F  T is T
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Properties of quantifiers
• x y is the same as y x
• x y is the same as y x
• x y is not the same as y x
• x y Loves(x,y)
• “There is a person who loves everyone in the world”
• More exactly: x y (person(x)  person(y)  Loves(x,y))
• y x Loves(x,y)
• “Everyone in the world is loved by at least one person”
• Quantifier duality: each can be expressed using the other
• x Likes(x,IceCream) x Likes(x,IceCream)
• x Likes(x,Broccoli) x Likes(x,Broccoli)
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Parentheses
• Parentheses are often used with quantifiers
• Unfortunately, everyone uses them differently, so don’t be upset at any
usage you see
• Examples:
• (x) person(x)  likes(x,iceCream)
• (x) (person(x)  likes(x,iceCream))
• (x) [ person(x)  likes(x,iceCream) ]
• x, person(x)  likes(x,iceCream)
• x (person(x)  likes(x,iceCream))
• I prefer parentheses that show the scope of the quantifier
• x (x > 0)  x (x < 0)
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More rules
• Now there are numerous additional rules we can apply!
• Here are two exceptionally important rules:
• x, p(x)  x, p(x)
“If not every x satisfies p(x), then there exists a x that does not satisfy
p(x)”
• x, p(x)  x, p(x)
“If there does not exist an x that satisfies p(x), then all x do not satisfy
p(x)”
• In any case, the search space is just too large to be feasible
• This was the case until 1970, when J. Robinson discovered resolution
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network
reasoning over time
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Logic by computer was infeasible
• Why is logic so hard?
• You start with a large collection of facts (predicates)
• You start with a large collection of possible transformations (rules)
• Some of these rules apply to a single fact to yield a new fact
• Some of these rules apply to a pair of facts to yield a new fact
• So at every step you must:
• Choose some rule to apply
• Choose one or two facts to which you might be able to apply the rule
• If there are n facts
• There are n potential ways to apply a single-operand rule
• There are n * (n - 1) potential ways to apply a two-operand rule
• Add the new fact to your ever-expanding fact base
• The search space is huge!
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The magic of resolution
• Here’s how resolution works:
• You transform each of your facts into a particular form, called a clause
(this is the tricky part)
• You apply a single rule, the resolution principle, to a pair of clauses
• Clauses are closed with respect to resolution--that is, when you
resolve two clauses, you get a new clause
• You add the new clause to your fact base
• So the number of facts you have grows linearly
• You still have to choose a pair of facts to resolve
• You never have to choose a rule, because there’s only one
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The fact base
• A fact base is a collection of “facts,” expressed in predicate calculus, that are presumed to be true (valid)
• These facts are implicitly “anded” together
• Example fact base:
• seafood(X)  likes(John, X) (where X is a variable)
• seafood(shrimp)
• pasta(X)  likes(Mary, X) (where X is a different variable)
• pasta(spaghetti)
• That is,
• (seafood(X)  likes(John, X))  seafood(shrimp) 
(pasta(Y)  likes(Mary, Y))  pasta(spaghetti)
• Notice that we had to change some Xs to Ys
• The scope of a variable is the single fact in which it occurs
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Clause form
• A clause is a disjunction ("or") of zero or more literals, some or all of
which may be negated
• Example:
sinks(X)  dissolves(X, water)  ¬denser(X, water)
• Notice that clauses use only “or” and “not”—they do not use “and,”
“implies,” or either of the quantifiers “for all” or “there exists”
• The impressive part is that any predicate calculus expression can be
put into clause form
• Existential quantifiers, , are the trickiest ones
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Unification
• From the pair of facts (not yet clauses, just facts):
• seafood(X)  likes(John, X) (where X is a variable)
• seafood(shrimp)
• We ought to be able to conclude
• likes(John, shrimp)
• We can do this by unifying the variable X with the constant shrimp
• This is the same “unification” as is done in Prolog
• This unification turns seafood(X)  likes(John, X) into
seafood(shrimp)  likes(John, shrimp)
• Together with the given fact seafood(shrimp), the final deductive
step is easy
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The resolution principle
• Here it is:
• From X  someLiterals
and X  someOtherLiterals
----------------------------------------------
conclude: someLiterals  someOtherLiterals
• That’s all there is to it!
• Example:
• broke(Bob)  well-fed(Bob)
¬broke(Bob)  ¬hungry(Bob)
--------------------------------------
well-fed(Bob)  ¬hungry(Bob)
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A common error
• You can only do one resolution at a time
• Example:
• broke(Bob)  well-fed(Bob)  happy(Bob)
¬broke(Bob)  ¬hungry(Bob) ∨ ¬happy(Bob)
• You can resolve on broke to get:
• well-fed(Bob)  happy(Bob)  ¬hungry(Bob)  ¬happy(Bob)  T
• Or you can resolve on happy to get:
• broke(Bob)  well-fed(Bob)  ¬broke(Bob)  ¬hungry(Bob)  T
• Note that both legal resolutions yield a tautology (a trivially true statement, containing X
 ¬X), which is correct but useless
• But you cannot resolve on both at once to get:
• well-fed(Bob)  ¬hungry(Bob)
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Contradiction
• A special case occurs when the result of a resolution (the resolvent) is
empty, or “NIL”
• Example:
• hungry(Bob)
¬hungry(Bob)
----------------
NIL
• In this case, the fact base is inconsistent
• This will turn out to be a very useful observation in doing resolution
theorem proving
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A first example
• “Everywhere that John goes, Rover goes. John is at school.”
• at(John, X)  at(Rover, X) (not yet in clause form)
• at(John, school) (already in clause form)
• We use implication elimination to change the first of these into clause
form:
• at(John, X)  at(Rover, X)
• at(John, school)
• We can resolve these on at(-, -), but to do so we have to unify X with
school; this gives:
• at(Rover, school)
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Refutation resolution
• The previous example was easy because it had very few clauses
• When we have a lot of clauses, we want to focus our search on the
thing we would like to prove
• We can do this as follows:
• Assume that our fact base is consistent (we can’t derive NIL)
• Add the negation of the thing we want to prove to the fact base
• Show that the fact base is now inconsistent
• Conclude the thing we want to prove
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Example of refutation resolution
• “Everywhere that John goes, Rover goes. John is at school. Prove that Rover is
at school.”
1. at(John, X)  at(Rover, X)
2. at(John, school)
3. at(Rover, school) (this is the added clause)
• Resolve #1 and #3:
4. at(John, X)
• Resolve #2 and #4:
5. NIL
• Conclude the negation of the added clause: at(Rover, school)
• This seems a roundabout approach for such a simple example, but it works well
for larger problems
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A second example
• Start with:
• it_is_raining  I_take_umbrella
• Convert to clause form:
1. it_is_raining  it_is_sunny
2. it_is_sunny  I_stay_dry
3. it_is_raining  I_take_umbrella
4. I_take_umbrella  I_stay_dry
• Prove that I stay dry:
5. I_stay_dry
• Proof:
6. (5, 2) it_is_sunny
7. (6, 1) it_is_raining
8. (5, 4) I_take_umbrella
9. (8, 3) it_is_raining
10. (9, 7) NIL
▪ Therefore, (I_stay_dry)
▪ I_stay_dry
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Converting sentences to CNF
1. Eliminate all ↔ connectives
(P ↔ Q)  ((P → Q) ^ (Q → P))
2. Eliminate all → connectives
(P → Q)  (P  Q)
3. Reduce the scope of each negation symbol to a single predicate
P  P
(P  Q)  P  Q
(P  Q)  P  Q
(x)P  (x)P
(x)P  (x)P
4. Standardize variables: rename all variables so that each quantifier has its own
unique variable name
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Converting sentences to clausal form Skolem
constants and functions
5. Eliminate existential quantification by introducing Skolem
constants/functions
(x)P(x)  P(c)
c is a Skolem constant (a brand-new constant symbol that is not used in any
other sentence)
(x)(y)P(x,y)  (x)P(x, f(x))
since  is within the scope of a universally quantified variable, use a Skolem
function f to construct a new value that depends on the universally
quantified variable
f must be a brand-new function name not occurring in any other sentence in
the KB.
E.g., (x)(y)loves(x,y)  (x)loves(x,f(x))
In this case, f(x) specifies the person that x loves
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Converting sentences to clausal form
6. Remove universal quantifiers by (1) moving them all to the left end;
(2) making the scope of each the entire sentence; and (3) dropping
the “prefix” part
Ex: (x)P(x)  P(x)
7. Put into conjunctive normal form (conjunction of disjunctions) using
distributive and associative laws
(P  Q)  R  (P  R)  (Q  R)
(P  Q)  R  (P  Q  R)
8. Split conjuncts into separate clauses
9. Standardize variables so each clause contains only variable names
that do not occur in any other clause
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An example
(x)(P(x) → ((y)(P(y) → P(f(x,y)))  (y)(Q(x,y) → P(y))))
2. Eliminate →
(x)(P(x)  ((y)(P(y)  P(f(x,y)))  (y)(Q(x,y)  P(y))))
3. Reduce scope of negation
(x)(P(x)  ((y)(P(y)  P(f(x,y))) (y)(Q(x,y)  P(y))))
4. Standardize variables
(x)(P(x)  ((y)(P(y)  P(f(x,y))) (z)(Q(x,z)  P(z))))
5. Eliminate existential quantification
(x)(P(x) ((y)(P(y)  P(f(x,y))) (Q(x,g(x))  P(g(x)))))
6. Drop universal quantification symbols
(P(x)  ((P(y)  P(f(x,y))) (Q(x,g(x))  P(g(x)))))
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Example
7. Convert to conjunction of disjunctions
(P(x)  P(y)  P(f(x,y)))  (P(x)  Q(x,g(x))) 
(P(x)  P(g(x)))
8. Create separate clauses
P(x)  P(y)  P(f(x,y))
P(x)  Q(x,g(x))
P(x)  P(g(x))
9. Standardize variables
P(x)  P(y)  P(f(x,y))
P(z)  Q(z,g(z))
P(w)  P(g(w))
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Running example
• All Romans who know Marcus either hate Caesar or
think that anyone who hates anyone is crazy
• x, [ Roman(x)  know(x, Marcus) ] 
[ hate(x, Caesar) 
(y, z, hate(y, z)  thinkCrazy(x, y))]
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Step 1: Eliminate implications
• Use the fact that x  y is equivalent to x  y
• x, [ Roman(x)  know(x, Marcus) ] 
[ hate(x, Caesar) 
(y, z, hate(y, z)  thinkCrazy(x, y))]
• x, [ Roman(x)  know(x, Marcus) ] 
[hate(x, Caesar) 
(y, (z, hate(y, z)  thinkCrazy(x, y))]
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Step 2: Reduce the scope of 
• Reduce the scope of negation to a single term, using:
• (p)  p
• (a  b)  (a  b)
• (a  b)  (a  b)
• x, p(x)  x, p(x)
• x, p(x)  x, p(x)
• x, [ Roman(x)  know(x, Marcus) ] 
(y, (z, hate(y, z)  thinkCrazy(x, y))]
• x, [ Roman(x)  know(x, Marcus) ] 
(y, z, hate(y, z)  thinkCrazy(x, y))]
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Step 3: Standardize variables apart
• x, P(x)  x, Q(x)
becomes
x, P(x)  y, Q(y)
• This is just to keep the scopes of variables from
getting confused
• Not necessary in our running example
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Step 4: Move quantifiers
• Move all quantifiers to the left, without changing their relative
positions
• x, [ Roman(x)  know(x, Marcus) ] 
(y, z, hate(y, z)  thinkCrazy(x, y)]
• x, y, z,[ Roman(x)  know(x, Marcus) ] 
(hate(y, z)  thinkCrazy(x, y))]
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Step 5: Eliminate existential quantifiers
• We do this by introducing Skolem functions:
•If x, p(x) then just pick one; call it x’
•If the existential quantifier is under control of a
universal quantifier, then the picked value has to be
a function of the universally quantified variable:
•If x, y, p(x, y) then x, p(x, y(x))
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Step 6: Drop the prefix (quantifiers)
• x, y, z,[ Roman(x)  know(x, Marcus) ] 
[hate(x, Caesar)  (hate(y, z)  thinkCrazy(x, y))]
• At this point, all the quantifiers are universal quantifiers
• We can just take it for granted that all variables are
universally quantified
• [ Roman(x)  know(x, Marcus) ] 
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Step 7: Create a conjunction of disjuncts
• [ Roman(x)  know(x, Marcus) ] 
becomes
Roman(x)  know(x, Marcus) 
hate(x, Caesar)  hate(y, z)  thinkCrazy(x, y)
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Step 8: Create separate clauses
• Every place we have an , we break our expression up
into separate pieces
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Step 9: Standardize apart
• Rename variables so that no two clauses have the same
variable
• Final result:
Roman(x)  know(x, Marcus) 
hate(x, Caesar)  hate(y, z)  thinkCrazy(x, y)
• That’s it! It’s a long process, but easy enough to do
mechanically
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Resolution
• Resolution is a sound and complete inference procedure for FOL
• Reminder: Resolution rule for propositional logic:
• P1  P2  ...  Pn
• P1  Q2  ...  Qm
• Resolvent: P2  ...  Pn  Q2  ...  Qm
• Examples
• P and  P  Q : derive Q (Modus Ponens)
• ( P  Q) and ( Q  R) : derive  P  R
• P and  P : derive False [contradiction!]
• (P  Q) and ( P   Q) : derive True
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Resolution in first-order logic
• Given sentences
P1  ...  Pn
Q1  ...  Qm
• in conjunctive normal form:
• each Pi and Qi is a literal, i.e., a positive or negated predicate symbol with its
terms,
• if Pj and Qk unify with substitution list θ, then derive the resolvent sentence:
subst(θ, P1 ...  Pj-1  Pj+1 ... Pn  Q1  …Qk-1  Qk+1 ...  Qm)
• Example
• from clause P(x, f(a))  P(x, f(y))  Q(y)
• and clause P(z, f(a))  Q(z)
• derive resolvent P(z, f(y))  Q(y)  Q(z)
• using θ = {x/z}
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Resolution refutation
• Given a consistent set of axioms KB and goal sentence Q, show that KB
|= Q
• Proof by contradiction: Add Q to KB and try to prove false.
i.e., (KB |- Q) ↔ (KB  Q |- False)
• Resolution is refutation complete: it can establish that a given sentence
Q is entailed by KB, but can’t (in general) be used to generate all logical
consequences of a set of sentences
• Also, it cannot be used to prove that Q is not entailed by KB.
• Resolution won’t always give an answer since entailment is only
semidecidable
• And you can’t just run two proofs in parallel, one trying to prove Q and the
other trying to prove Q, since KB might not entail either one
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Refutation resolution proof tree
allergies(w) v sneeze(w) cat(y) v ¬allergic-to-cats(z)  allergies(z)
cat(y) v sneeze(z)  ¬allergic-to-cats(z) cat(Felix)
sneeze(z) v ¬allergic-to-cats(z) allergic-to-cats(Lise)
false
sneeze(Lise)
sneeze(Lise)
w/z
y/Felix
z/Lise
negated query
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We need answers to the following questions
• How to convert FOL sentences to conjunctive normal form (a.k.a. CNF,
clause form): normalization and skolemization
• How to unify two argument lists, i.e., how to find their most general
unifier (mgu) q: unification
• How to determine which two clauses in KB should be resolved next
(among all resolvable pairs of clauses) : resolution (search) strategy
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Unification
• Unification is a “pattern-matching” procedure
• Takes two atomic sentences, called literals, as input
• Returns “Failure” if they do not match and a substitution list, θ, if they do
• That is, unify(p,q) = θ means subst(θ, p) = subst(θ, q) for two atomic
sentences, p and q
• θ is called the most general unifier (mgu)
• All variables in the given two literals are implicitly universally
quantified
• To make literals match, replace (universally quantified) variables by
terms
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Unification algorithm
procedure unify(p, q, θ)
Scan p and q left-to-right and find the first corresponding
terms where p and q “disagree” (i.e., p and q not equal)
If there is no disagreement, return θ (success!)
Let r and s be the terms in p and q, respectively,
where disagreement first occurs
If variable(r) then {
Let θ = union(θ, {r/s})
Return unify(subst(θ, p), subst(θ, q), θ)
} else if variable(s) then {
Let θ = union(θ, {s/r})
Return unify(subst(θ, p), subst(θ, q), θ)
} else return “Failure”
end
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Unification: Remarks
• Unify is a linear-time algorithm that returns the most
general unifier (mgu), i.e., the shortest-length substitution
list that makes the two literals match.
• In general, there is not a unique minimum-length
substitution list, but unify returns one of minimum length
• A variable can never be replaced by a term containing that
variable
Example: x/f(x) is illegal.
• This “occurs check” should be done in the above pseudo-
code before making the recursive calls
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Unification examples
• Example:
• parents(x, father(x), mother(Bill))
• parents(Bill, father(Bill), y)
• {x/Bill, y/mother(Bill)}
• Example:
• parents(x, father(x), mother(Bill))
• parents(Bill, father(y), z)
• {x/Bill, y/Bill, z/mother(Bill)}
• Example:
• parents(x, father(x), mother(Jane))
• parents(Bill, father(y), mother(y))
• Failure
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Practice example : Did Curiosity kill the cat
• Jack owns a dog. Every dog owner is an animal lover. No animal lover
kills an animal. Either Jack or Curiosity killed the cat, who is named
Tuna. Did Curiosity kill the cat?
• These can be represented as follows:
A. (x) Dog(x)  Owns(Jack,x)
B. (x) ((y) Dog(y)  Owns(x, y)) → AnimalLover(x)
C. (x) AnimalLover(x) → ((y) Animal(y) → Kills(x,y))
D. Kills(Jack,Tuna)  Kills(Curiosity,Tuna)
E. Cat(Tuna)
F. (x) Cat(x) → Animal(x)
G. Kills(Curiosity, Tuna)
GOAL
Resolution example
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• Convert to clause form
A1. (Dog(D))
A2. (Owns(Jack,D))
B. (Dog(y), Owns(x, y), AnimalLover(x))
C. (AnimalLover(a), Animal(b), Kills(a,b))
D. (Kills(Jack,Tuna), Kills(Curiosity,Tuna))
E. Cat(Tuna)
F. (Cat(z), Animal(z))
• Add the negation of query:
G: (Kills(Curiosity, Tuna))
D is a skolem constant
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• The resolution refutation proof
R1: G, D, {} (Kills(Jack, Tuna))
R2: R1, C, {a/Jack, b/Tuna} (~AnimalLover(Jack),
~Animal(Tuna))
R3: R2, B, {x/Jack} (~Dog(y), ~Owns(Jack, y),
~Animal(Tuna))
R4: R3, A1, {y/D} (~Owns(Jack, D),
~Animal(Tuna))
R5: R4, A2, {} (~Animal(Tuna))
R6: R5, F, {z/Tuna} (~Cat(Tuna))
R7: R6, E, {} FALSE
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106
• The proof tree
G D
C
B
A1
A2
F
A
R1: K(J,T)
R2: AL(J)  A(T)
R3: D(y)  O(J,y)  A(T)
R4: O(J,D), A(T)
R5: A(T)
R6: C(T)
R7: FALSE
{}
{a/J,b/T}
{x/J}
{y/D}
{}
{z/T}
{}
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Table of Contents
• Unification and Resolution
• Knowledge representation using rules-Knowledge representation using semantic
nets
network
• Probabilistic reasoning-Probabilistic reasoning over time-Probabilistic reasoning
over time
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Production Rules
• Condition-Action Pairs
• IF this condition (or premise or antecedent) occurs,
THEN some action (or result, or conclusion, or
consequence) will (or should) occur
• IF the traffic light is red AND you have stopped,
THEN a right turn is OK

Production Rules
• Each production rule in a knowledge base represents an
autonomous chunk of expertise
• When combined and fed to the inference engine, the set of rules
behaves synergistically
• Rules can be viewed as a simulation of the cognitive behaviour
of human experts
• Rules represent a model of actual human behaviour
• Predominant technique used in expert systems, often in
conjunction with frames

Forms of Rules
• IF premise, THEN conclusion
• IF your income is high, THEN your chance of being
audited by the Inland Revenue is high
• Conclusion, IF premise
• Your chance of being audited is high, IF your income
is high

Forms of Rules
• Inclusion of ELSE
• IF your income is high, OR your deductions are unusual, THEN
your chance of being audited is high, OR ELSE your chance of
being audited is low
• More complex rules
• IF credit rating is high AND salary is more than £30,000, OR
assets are more than £75,000, AND pay history is not "poor,"
THEN approve a loan up to £10,000, and list the loan in category
"B.”
• Action part may have more information: THEN "approve the loan"
and "refer to an agent"

Characteristics of Rules
First Part Second Part
Names Premise
Antecedent
Situation
IF
Conclusion
Consequence
Action
THEN
Nature Conditions, similar to declarative knowledge Resolutions, similar to procedural knowledge
Size Can have many IFs Usually only one conclusion
Statement AND statements All conditions must be true for a conclusion to be true
OR statements If any condition is true, the conclusion is true

Rule-based Inference
• Production rules are typically used as part of a
production system
• Production systems provide pattern-directed control of
the reasoning process
• Production systems have:
• Productions: set of production rules
• Working Memory (WM): description of current state
of the world
• Recognise-act cycle

Production Systems
Production
Rules
C1→A1
C2→A2
C3→A3
…
Cn→An
Working
Memory
Conflict
Resolution
Conflict
Set
Environment

Recognise-Act Cycle
• Patterns in WM matched against production rule conditions
• Matching (activated) rules form the conflict set
• One of the matching rules is selected (conflict resolution) and
fired
• Action of rule is performed
• Contents of WM updated
• Cycle repeats with updated WM

Conflict Resolution
• Reasoning in a production system can be viewed as a type of
search
• Selection strategy for rules from the conflict set controls
search
• Production system maintains the conflict set as an agenda
• Ordered list of activated rules (those with their conditions
satisfied) which have not yet been executed
• Conflict resolution strategy determines where a newly-
activated rule is inserted

Salience
• Rules may be given a precedence order by assigning a
salience value
• Newly activated rules are placed in the agenda above all rules
of lower salience, and below all rules with higher salience
• Rule with higher salience are executed first
• Conflict resolution strategy applies between rules of the
same salience
• If salience and the conflict resolution strategy can ’ t
determine which rule is to be executed next, a rule is chosen
at random from the most highly ranked rules

Conflict Resolution Strategies
• Depth-first: newly activated rules placed above other rules in the
agenda
• Breadth-first: newly activated rules placed below other rules
• Specificity: rules ordered by the number of conditions in the LHS
(simple-first or complex-first)
• Least recently fired: fire the rule that was last fired the longest time
ago
• Refraction: don’t fire a rule unless the WM patterns that match its
conditions have been modified
• Recency: rules ordered by the timestamps on the facts that match
their conditions

Salience
• Salience facilitates the modularization of expert systems
in which modules work at different levels of abstraction
• Over-use of salience can complicate a system
• Explicit ordering to rule execution
• Makes behaviour of modified systems less predictable
• Rule of thumb: if two rules have the same salience, are
in the same module, and are activated concurrently,
then the order in which they are executed should not
matter

Common Types of Rules
• Knowledge rules, or declarative rules, state all the facts
and relationships about a problem
• Inference rules, or procedural rules, advise on how to
solve a problem, given that certain facts are known
• Inference rules contain rules about rules (metarules)
• Knowledge rules are stored in the knowledge base
• Inference rules become part of the inference engine
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Major Advantages of Rules
• Easy to understand (natural form of knowledge)
• Easy to derive inference and explanations
• Easy to modify and maintain
• Easy to combine with uncertainty
• Rules are frequently independent
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Major Limitations of Rules
• Complex knowledge requires many rules
• Search limitations in systems with many rules
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Table of Contents
nets
network
over time
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Semantic Networks
• A semantic network is a structure for representing
knowledge as a pattern of interconnected nodes and
arcs
• Nodes in the net represent concepts of entities,
attributes, events, values
• Arcs in the network represent relationships that hold
between the concepts
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Semantic Networks
• Semantic networks can show inheritance
• Relationship types – is-a, has-a
• Semantic Nets - visual representation of relationships
• Can be combined with other representation methods
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Semantic Networks
Animal
Can breathe
Can eat
Has skin
Bird
Can fly
Has wings
Has feathers
Canary
Can sing
Is yellow
Ostrich
Runs fast
Cannot fly
Is tall
Fish
Can swim
Has fins
Has gills
Salmon
Swims upstream
Is pink
Is edible
is-a
is-a
is-a
is-a
is-a
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DOG
ANIMAL
HOUND
BEAGLE
SNOOPY
COLLIE
LASSIE
SHEEPDOG
is a
is a
is a
is a
barks is a
instance
instance
CHARLIE BROWN
FICTIONAL
CHARACTER instance
instance
instance
has tail
moves
breathes
size: medium
size: small
works sheep
tracks
friend of
Semantic Networks
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Semantic Networks
What does or should a node represent?
•A class of objects?
•An instance of an class?
•The canonical instance of a class?
•The set of all instances of a class?
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Semantic Networks
• Semantics of links that define new objects and links that relate
existing objects, particularly those dealing with ‘intrinsic’
characteristics of a given object
• How does one deal with the problems of comparison between
objects (or classes of objects) through their attributes?
• Essentially the problem of comparing object instances
• What mechanisms are there are to handle quantification in
semantic network formalisms?
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Transitive inference, but…
• Clyde is an elephant, an elephant is a mammal: Clyde is a
mammal.
• The US President is elected every 4 years, Bush is US President:
Bush is elected every 4 years
• My car is a Ford, Ford is a car company: my car is a car
company
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Table of Contents
nets
network
over time
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Frames
• A frame is a knowledge representation formalism based on the idea of a
frame of reference.
• A frame is a data structure that includes all the knowledge about a
particular object
• Frames organised in a hierarchy Form of object-oriented programming for
AI and ES.
• Each frame describes one object
• Special terminology
M. Minsky (1974) A Framework for Representing Knowledge,
MIT-AI Laboratory Memo 306
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Frames
• There are two types of frame:
• Class Frame
• Individual or Instance Frame
• A frame carries with it a set of slots that can represent
objects that are normally associated with a subject of
the frame.
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Frames
• The slots can then point to other slots or frames. That
gives frame systems the ability to carry out inheritance
and simple kinds of data manipulation.
• The use of procedures - also called demons in the
literature - helps in the incorporation of substantial
amounts of procedural knowledge into a particular
frame-oriented knowledge base
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Frame-based model of semantic
memory
• Knowledge is organised in a data structure
• Slots in structure are instantiated with particular values for a
given instance of data
• ...translation to OO terminology:
• frames == classes or objects
• slots == variables/methods
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General Knowledge as Frames
DOG
Fixed
legs: 4
Default
diet: carnivorous
sound: bark
Variable
size:
colour:
COLLIE
Fixed
breed of: DOG
type: sheepdog
Default
size: 65cm
Variable
colour:
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MAMMAL:
subclass: ANIMAL
has_part: head
ELEPHANT
subclass: MAMMAL
colour: grey
size: large
Nellie
instance: ELEPHANT
likes: apples
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General Knowledge as Frames

Logic underlies Frames
• ∀x mammal(x) ⇒ has_part(x, head)
• ∀x elephant(x) ⇒ mammal(x)
• elephant(clyde)
∴
mammal(clyde)
has_part(clyde, head)
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MAMMAL:
subclass: ANIMAL
has_part: head
*furry: yes
ELEPHANT
subclass: MAMMAL
has_trunk: yes
*colour: grey
*size: large
*furry: no
Clyde
instance: ELEPHANT
colour: pink
owner: Fred
Nellie
instance: ELEPHANT
size: small
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Logic underlies Frames

Frames (Contd.)
• Can represent subclass and instance relationships (both
sometimes called ISA or “is a”)
• Properties (e.g. colour and size) can be referred to as slots and
slot values (e.g. grey, large) as slot fillers
• Objects can inherit all properties of parent class (therefore
Nellie is grey and large)
• But can inherit properties which are only typical (usually called
default, here starred), and can be overridden
• For example, mammal is typically furry, but this is not so for an
elephant
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• Provide a concise, structural representation of knowledge in a
natural manner
• Frame encompasses complex objects, entire situations or a
management problem as a single entity
• Frame knowledge is partitioned into slots
• Slot can describe declarative knowledge or procedural
knowledge
• Hierarchy of Frames: Inheritance
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Frames (Contd.)

Capabilities of Frames
• Ability to clearly document information about a domain model;
for example, a plant's machines and their associated attributes
• Related ability to constrain allowable values of an attribute
• Modularity of information, permitting ease of system expansion
and maintenance
• More readable and consistent syntax for referencing domain
objects in the rules
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Capabilities of Frames
• Platform for building graphic interface with object graphics
• Mechanism to restrict the scope of facts considered during
forward or backward chaining
• Access to a mechanism that supports the inheritance of
information down a class hierarchy
• Used as underlying model in standards for accessing KBs (Open
Knowledge Base Connectivity - OKBC)
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Summary
• Frames have been used in conjunction with other, less well-
grounded, representation formalisms, like production systems,
when used to build to pre-operational or operational expert
systems
• Frames cannot be used efficiently to organise ‘a whole
computation
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Table of Contents
• Knowledge and reasoning-Approaches and issues of knowledge
reasoning-Knowledge base agents
• Logic Basics-Logic-Propositional logic-syntax ,semantics and
inferences-Propositional logic- Reasoning patterns
• Unification and Resolution-Knowledge representation using rules-
Knowledge representation using semantic nets
• Uncertain Knowledge and reasoning-Methods-Bayesian probability and
belief network
• Probabilistic reasoning-Probabilistic reasoning over time
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Types of Inference
• Deduction
• Induction
• Abduction
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Deduction
• Deriving a conclusion from given axioms and facts
• Also called logical inference or truth preservation
Axiom – All kids are naughty
Fact/Premise – Riya is a kid
Conclusion – Riya is naughty

Induction
• Deriving general rule or axiom from background knowledge and
observations
• Riya is a kid
• Riya is naughty
General axiom which is derived is:
Kids are naughty

Abduction
• A premise is derived from a known axiom and some observations
• All kids are naughty
• Riya is naughty
Inference
Riya is a kid

Table of Contents
belief network
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Uncertain knowledge and reasoning
• In real life, it is not always possible to determine the state of the environment as it might not be clear. Due
to partially observable or non-deterministic environments, agents may need to handle uncertainty and deal
with it.
• Uncertain data: Data that is missing, unreliable, inconsistent or noisy
• Uncertain knowledge: When the available knowledge has multiple causes leading to multiple effects or
incomplete knowledge of causality in the domain
• Uncertain knowledge representation: The representations which provides a restricted model of the real
system, or has limited expressiveness
• Inference: In case of incomplete or default reasoning methods, conclusions drawn might not be completely
accurate. Let’s understand this better with the help of an example.
• IF primary infection is bacteria cea
• AND site of infection is sterile
• AND entry point is gastrointestinal tract
• THEN organism is bacteriod (0.7).
• In such uncertain situations, the agent does not guarantee a solution but acts on its own
assumptions and probabilities and gives some degree of belief that it will reach the required
solution.
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• For example, In case of Medical diagnosis consider the rule Toothache = Cavity. This
is not complete as not all patients having toothache have cavities. So we can write a
more generalized rule Toothache = Cavity V Gum problems V Abscess… To make this
rule complete, we will have to list all the possible causes of toothache. But this is not
feasible due to the following rules:
• Laziness- It will require a lot of effort to list the complete set of antecedents and
consequents to make the rules complete.
• Theoretical ignorance- Medical science does not have complete theory for the
domain
• Practical ignorance- It might not be practical that all tests have been or can be
conducted for the patients.
• Such uncertain situations can be dealt with using
Probability theory
Truth Maintenance systems
Fuzzy logic.
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Probability
• Probability is the degree of likeliness that an event will occur. It provides a certain degree of belief
in case of uncertain situations. It is defined over a set of events U and assigns value P(e) i.e.
probability of occurrence of event e in the range [0,1]. Here each sentence is labeled with a real
number in the range of 0 to 1, 0 means the sentence is false and 1 means it is true.
• Conditional Probability or Posterior Probability is the probability of event A given that B has
already occurred.
• P(A|B) = (P(B|A) * P(A)) / P(B)
• For example, P(It will rain tomorrow| It is raining today) represents conditional probability of it
raining tomorrow as it is raining today.
• P(A|B) + P(NOT(A)|B) = 1
• Joint probability is the probability of 2 independent events happening simultaneously like rolling
two dice or tossing two coins together. For example, Probability of getting 2 on one dice and 6 on
the other is equal to 1/36. Joint probability has a wide use in various fields such as physics,
astronomy, and comes into play when there are two independent events. The full joint probability
distribution specifies the probability of each complete assignment of values to random variables.
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Bayes Theorem
• It is based on the principle that every pair of features being
classified is independent of each other. It calculates probability
P(A|B) where A is class of possible outcomes and B is given
instance which has to be classified.
• P(A|B) = P(B|A) * P(A) / P(B)
• P(A|B) = Probability that A is happening, given that B has
occurred (posterior probability)
• P(A) = prior probability of class
• P(B) = prior probability of predictor
• P(B|A) = likelihood
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Consider the following data.
Depending on the weather
(sunny, rainy or overcast), the
children will play(Y) or not
play(N).
Here, the total number of
observations = 14
Probability that children will
play given that weather is
sunny :
P( Yes| Sunny) = P(Sunny |
Yes) * P(Yes) / P(Sunny)
= 0.33 * 0.64 / 0.36
= 0.59
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It is a probabilistic graphical model for
representing uncertain domain and to
reason under uncertainty. It consists of
nodes representing variables, arcs
representing direct connections between
them, called causal correlations. It
represents conditional dependencies
between random variables through a
Directed Acyclic Graph (DAG). A belief
network consist of:
1. A DAG with nodes labeled with variable
names,
2. Domain for each random variable,
3. Set of conditional probability tables for
each variable given its parents, including
prior probability for nodes with no
parents.
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• Let’s have a look at the steps followed.
1. Identify nodes which are the random variables and the possible
values they can have from the probability domain. The nodes can be
boolean (True/ False), have ordered values or integral values.
2. Structure- It is used to represent causal relationships between the
variables. Two nodes are connected if one affects or causes the other
and the arc points towards the effect. For instance, if it is windy or
cloudy, it rains. There is a direct link from Windy/Cloudy to Rains.
Similarly, from Rains to Wet grass and Leave, i.e., if it rains, grass will
be wet and leave is taken from work.
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3. Probability- Quantifying relationship between nodes. Conditional
Probability:
• P(A^B) = P(A|B) * P(B)
• P(A|B) = P(B|A) * P(A)
• P(B|A) = P(A|B) * P(B) / P(A)
• Joint probability:
4. Markov property- Bayesian Belied Networks require assumption of
Markov property, i.e., all direct dependencies are shown by using arcs.
Here there is no direct connection between it being Cloudy and Taking a
leave. But there is one via Rains. Belief Networks which have Markov
property are also called independence maps.
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Inference in Belief Networks
• Bayesian Networks provide various types of representations of probability distribution over their
variables. They can be conditioned over any subset of their variables, using any direction of
reasoning.
• For example, one can perform diagnostic reasoning, i.e. when it Rains, one can update his belief
about the grass being wet or if leave is taken from work. In this case reasoning occurs in the
opposite direction to the network arcs. Or one can perform predictive reasoning, i.e., reasoning
from new information about causes to new beliefs about effects, following direction of the arcs.
For example, if the grass is already wet, then the user knows that it has rained and it might have
been cloudy or windy. Another form of reasoning involves reasoning about mutual causes of a
common effect. This is called inter causal reasoning.
• There are two possible causes of an effect, represented in the form of a ‘V’. For example, the
common effect ‘Rains’ can be caused by two reasons ‘Windy’ and ‘Cloudy.’ Initially, the two
causes are independent of each other but if it rains, it will increase the probability of both the
causes. Assume that we know it was windy. This information explains the reasons for the rainfall
and lowers probability that it was cloudy.
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Table of Contents
belief network
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Bayesian probability and belief network
• Bayesian belief network is key computer technology for dealing with
probabilistic events and to solve a problem which has uncertainty. We
can define a Bayesian network as:
• "A Bayesian network is a probabilistic graphical model which
represents a set of variables and their conditional dependencies using
a directed acyclic graph."
• It is also called a Bayes network, belief network, decision network,
or Bayesian model.
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• Bayesian networks are probabilistic, because these networks are built from
a probability distribution, and also use probability theory for prediction and
anomaly detection.
• Real world applications are probabilistic in nature, and to represent the
relationship between multiple events, we need a Bayesian network. It can also be
used in various tasks including prediction, anomaly detection, diagnostics,
automated insight, reasoning, time series prediction, and decision making
under uncertainty.
• Bayesian Network can be used for building models from data and experts
opinions, and it consists of two parts:
Directed Acyclic Graph
Table of conditional probabilities.
• The generalized form of Bayesian network that represents and solve decision
problems under uncertain knowledge is known as an Influence diagram.
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Directed Acyclic Graph
A Bayesian network graph is made up of nodes
and Arcs (directed links), where:
Each node corresponds to the random variables,
and a variable can be continuous or discrete.
Arc or directed arrows represent the causal
relationship or conditional probabilities between
random variables. These directed links or arrows
connect the pair of nodes in the graph.
These links represent that one node directly
influence the other node, and if there is no
directed link that means that nodes are
independent with each other
In the above diagram, A, B, C, and D are
random variables represented by the nodes
of the network graph.
If we are considering node B, which is
connected with node A by a directed arrow,
then node A is called the parent of Node B.
Node C is independent of node A.
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CONDITIONAL PROBABILITY
• The Bayesian network has mainly two components:
Causal Component
Actual numbers
• Each node in the Bayesian network has condition probability
distribution P(Xi |Parent(Xi) ), which determines the effect of the
parent on that node.
• Bayesian network is based on Joint probability distribution and
conditional probability. So let's first understand the joint probability
distribution:
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Joint probability distribution:
• If we have variables x1, x2, x3,....., xn, then the probabilities of a
different combination of x1, x2, x3.. xn, are known as Joint probability
distribution.
• P[x1, x2, x3,....., xn], it can be written as the following way in terms of
the joint probability distribution.
• = P[x1| x2, x3,....., xn]P[x2, x3,....., xn]
• = P[x1| x2, x3,....., xn]P[x2|x3,....., xn]....P[xn-1|xn]P[xn].
• In general for each variable Xi, we can write the equation as:
• P(Xi|Xi-1,........., X1) = P(Xi |Parents(Xi ))
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Explanation of Bayesian network:
• Let's understand the Bayesian network through an example by creating a
directed acyclic graph:
Example: Harry installed a new burglar alarm at his home to detect burglary.
The alarm reliably responds at detecting a burglary but also responds for
minor earthquakes. Harry has two neighbors David and Sophia, who have
taken a responsibility to inform Harry at work when they hear the alarm.
David always calls Harry when he hears the alarm, but sometimes he got
confused with the phone ringing and calls at that time too. On the other
hand, Sophia likes to listen to high music, so sometimes she misses to hear
the alarm. Here we would like to compute the probability of Burglary
Alarm.
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Problem:
• Calculate the probability that alarm has sounded, but there is neither a burglary, nor an
earthquake occurred, and David and Sophia both called the Harry.
Solution:
• The Bayesian network for the above problem is given below. The network structure is showing
that burglary and earthquake is the parent node of the alarm and directly affecting the probability
of alarm's going off, but David and Sophia's calls depend on alarm probability.
• The network is representing that our assumptions do not directly perceive the burglary and also
do not notice the minor earthquake, and they also not confer before calling.
• The conditional distributions for each node are given as conditional probabilities table or CPT.
• Each row in the CPT must be sum to 1 because all the entries in the table represent an exhaustive
set of cases for the variable.
• In CPT, a boolean variable with k boolean parents contains 2K probabilities. Hence, if there are
two parents, then CPT will contain 4 probability values
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List of all events occurring in this network:
• Burglary (B)
• Earthquake(E)
• Alarm(A)
• David Calls(D)
• Sophia calls(S)
We can write the events of problem statement in the form of probability: P[D, S, A, B, E], can
rewrite the above probability statement using joint probability distribution:
• P[D, S, A, B, E]= P[D | S, A, B, E]. P[S, A, B, E]
• =P[D | S, A, B, E]. P[S | A, B, E]. P[A, B, E]
• = P [D| A]. P [ S| A, B, E]. P[ A, B, E]
• = P[D | A]. P[ S | A]. P[A| B, E]. P[B, E]
• = P[D | A ]. P[S | A]. P[A| B, E]. P[B |E]. P[E]
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Let's take the observed probability for
the Burglary and earthquake
component:
P(B= True) = 0.002, which is the
probability of burglary.
P(B= False)= 0.998, which is the
probability of no burglary.
P(E= True)= 0.001, which is the
probability of a minor earthquake
P(E= False)= 0.999, Which is the
probability that an earthquake not
occurred.
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B E P(A= True) P(A= False)
True True 0.94 0.06
True False 0.95 0.04
False True 0.31 0.69
False False 0.001 0.999
We can provide the
conditional probabilities as
per the below tables:
Conditional probability table
for Alarm A:
The Conditional probability of
Alarm A depends on Burglar
and earthquake:
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A P(D= True) P(D= False)
True 0.91 0.09
False 0.05 0.95
Conditional probability table
for David Calls:
The Conditional probability of
David that he will call
depends on the probability of
Alarm.
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A P(S= True) P(S= False)
True 0.75 0.25
False 0.02 0.98
Conditional probability
table for Sophia Calls:
The Conditional
probability of Sophia that
she calls is depending on
its Parent Node "Alarm."
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AP(S= True)P(S=
False)True0.750.25False0.020.98 AP(S=
True)P(S=

• From the formula of joint distribution, we can write the problem statement in the form of
probability distribution:
• P(S, D, A, ¬B, ¬E) = P (S|A) *P (D|A)*P (A|¬B ^ ¬E) *P (¬B) *P (¬E).
= 0.75* 0.91* 0.001* 0.998*0.999
= 0.00068045.
Hence, a Bayesian network can answer any query about the domain by using Joint distribution.
• The semantics of Bayesian Network:
• There are two ways to understand the semantics of the Bayesian network, which is given below:
1. To understand the network as the representation of the Joint probability distribution.
• It is helpful to understand how to construct the network.
2. To understand the network as an encoding of a collection of conditional independence
statements.
• It is helpful in designing inference procedure.
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Bayes' theorem in Artificial intelligence
Bayes' theorem:
• Bayes' theorem is also known as Bayes' rule, Bayes' law, or Bayesian
reasoning, which determines the probability of an event with uncertain
knowledge.
• In probability theory, it relates the conditional probability and marginal
probabilities of two random events.
• Bayes' theorem was named after the British mathematician Thomas Bayes.
The Bayesian inference is an application of Bayes' theorem, which is
fundamental to Bayesian statistics.
• It is a way to calculate the value of P(B|A) with the knowledge of P(A|B).
• Bayes' theorem allows updating the probability prediction of an event by
observing new information of the real world.
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Example: If cancer corresponds to one's age then by using Bayes' theorem, we can determine the probability of cancer more accurately with
the help of age.
• Bayes' theorem can be derived using product rule and conditional probability of event A with known event B:
• As from product rule we can write:
• P(A ⋀ B)= P(A|B) P(B) or
• Similarly, the probability of event B with known event A:
• P(A ⋀ B)= P(B|A) P(A)
• Equating right hand side of both the equations, we will get:
The above equation (a) is called as Bayes' rule or Bayes' theorem. This equation is basic of most modern AI systems for probabilistic inference.
• It shows the simple relationship between joint and conditional probabilities. Here,
• P(A|B) is known as posterior, which we need to calculate, and it will be read as Probability of hypothesis A when we have occurred an
evidence B.
• P(B|A) is called the likelihood, in which we consider that hypothesis is true, then we calculate the probability of evidence.
• P(A) is called the prior probability, probability of hypothesis before considering the evidence
• P(B) is called marginal probability, pure probability of an evidence.
• In the equation (a), in general, we can write P (B) = P(A)*P(B|Ai), hence the Bayes' rule can be written as:
• Where A1, A2, A3,........, An is a set of mutually exclusive and exhaustive events.
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Bayes' theorem in Artificial intelligence

Applying Bayes' rule:
• Bayes' rule allows us to compute the single term P(B|A) in terms of P(A|B), P(B), and P(A). This is very useful
in cases where we have a good probability of these three terms and want to determine the fourth one.
Suppose we want to perceive the effect of some unknown cause, and want to compute that cause, then the
Bayes' rule becomes:
Example-1:
Question: what is the probability that a patient has diseases meningitis with a stiff neck?
• Given Data:
A doctor is aware that disease meningitis causes a patient to have a stiff neck, and it occurs 80% of
the time. He is also aware of some more facts, which are given as follows:
The Known probability that a patient has meningitis disease is 1/30,000.
The Known probability that a patient has a stiff neck is 2%.
Let a be the proposition that patient has stiff neck and b be the proposition that patient has meningitis. , so we
can calculate the following as:
P(a|b) = 0.8
P(b) = 1/30000
P(a)= .02
• Hence, we can assume that 1 patient out of 750 patients has meningitis disease with a stiff neck.
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Applying Bayes' theorem in Artificial intelligence

Applying Bayes' theorem in Artificial
intelligence
Example-2:
Question: From a standard deck of playing cards, a single card is drawn. The probability that the
card is king is 4/52, then calculate posterior probability P(King|Face), which means the drawn
face card is a king card.
Solution:
P(king): probability that the card is King= 4/52= 1/13
P(face): probability that a card is a face card= 3/13
P(Face|King): probability of face card when we assume it is a king = 1
Putting all values in equation (i) we will get:
•
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Application of Bayes' theorem in Artificial
intelligence:
Following are some applications of Bayes' theorem:
• It is used to calculate the next step of the robot when the already
executed step is given.
• Bayes' theorem is helpful in weather forecasting.
• It can solve the Monty Hall problem.
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Table of Contents
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Probabilistic reasoning
Uncertainty:
• Till now, we have learned knowledge representation using first-order logic and propositional logic with
certainty, which means we were sure about the predicates. With this knowledge representation, we might
write A→B, which means if A is true then B is true, but consider a situation where we are not sure about
whether A is true or not then we cannot express this statement, this situation is called uncertainty.
• So to represent uncertain knowledge, where we are not sure about the predicates, we need uncertain
reasoning or probabilistic reasoning.
Causes of uncertainty:
Following are some leading causes of uncertainty to occur in the real world.
• Information occurred from unreliable sources.
• Experimental Errors
• Equipment fault
• Temperature variation
• Climate change.
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Probabilistic reasoning:
• Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty
in knowledge. In probabilistic reasoning, we combine probability theory with logic to handle the uncertainty.
• We use probability in probabilistic reasoning because it provides a way to handle the uncertainty that is the result of someone's
laziness and ignorance.
• In the real world, there are lots of scenarios, where the certainty of something is not confirmed, such as "It will rain today,"
"behavior of someone for some situations," "A match between two teams or two players." These are probable sentences for which
we can assume that it will happen but not sure about it, so here we use probabilistic reasoning.
Need of probabilistic reasoning in AI:
• When there are unpredictable outcomes.
• When specifications or possibilities of predicates becomes too large to handle.
• When an unknown error occurs during an experiment.
In probabilistic reasoning, there are two ways to solve problems with uncertain knowledge:
• Bayes' rule
• Bayesian Statistics
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As probabilistic reasoning uses probability and related terms, so before understanding probabilistic reasoning, let's
understand some common terms:
Probability: Probability can be defined as a chance that an uncertain event will occur. It is the numerical measure of the
likelihood that an event will occur. The value of probability always remains between 0 and 1 that represent ideal
uncertainties.
0 ≤ P(A) ≤ 1, where P(A) is the probability of an event A.
P(A) = 0, indicates total uncertainty in an event A.
P(A) =1, indicates total certainty in an event A.
We can find the probability of an uncertain event by using the below formula.
P(¬A) = probability of a not happening event.
P(¬A) + P(A) = 1.
• Event: Each possible outcome of a variable is called an event.
• Sample space: The collection of all possible events is called sample space.
• Random variables: Random variables are used to represent the events and objects in the real world.
• Prior probability: The prior probability of an event is probability computed before observing new information.
• Posterior Probability: The probability that is calculated after all evidence or information has taken into account. It is a
combination of prior probability and new information.
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Conditional probability:
• Conditional probability is a probability of occurring an event when another event
has already happened.
Let's suppose, we want to calculate the event A when event B has already occurred,
"the probability of A under the conditions of B", it can be written as:
P(A/B)=P(A ⋀B)/P(B)
• Where P(A⋀B)= Joint probability of a and B
• P(B)= Marginal probability of B.
• If the probability of A is given and we need to find the probability of B, then it will
be given as: P(B/A)=P(A ⋀B)/P(A)
• It can be explained by using the below Venn diagram, where B is occurred event,
so sample space will be reduced to set B, and now we can only calculate event A
when event B is already occurred by dividing the probability of P(A⋀B) by P( B ).
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Example:
In a class, there are 70% of the
students who like English and 40% of
the students who likes English and
mathematics, and then what is the
percent of students those who like
English also like mathematics?
Solution:
Let, A is an event that a student likes
Mathematics
B is an event that a student likes
English.
Hence, 57% are the students who
like English also like Mathematics.
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Table of Contents
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Probabilistic reasoning over time
Definition
• Probabilistic reasoning is the representation of knowledge where the concept of probability is applied to indicate the uncertainty
in knowledge.
Reasons to use Probabilistic Reasoning in AI
• Some reasons to use this way of representing knowledge is given below:
• When we are unsure of the predicates.
• When the possibilities of predicates become too large to list down.
• When during an experiment, it is proven that an error occurs.
• Probability of a given event = Chances of that event occurring / Total number of Events.
Notations and Properties
• Consider the statement S: March will be cold.
• Probability is often denoted as P(predicate).
• Considering the chances of March being cold is only 30%, therefore, P(S) = 0.3
• Probability always takes a value between 0 and 1. If the probability is 0, then the event will never occur and if it is 1, then it will
occur for sure.
• Then, P(¬S) = 0.7
• This means, the probability of March not being cold is 70%.
• Property 1: P(S) + P(¬S) = 1
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Consider the statement T: April will be cold.
• Then, P(S∧T) means Probability of S AND T, i.e., Probability of March and April being cold.
• P(S∨T) means Probability of S OR T, i.e., Probability of March or April being cold.
• Property 2: P(S∨T) = P(S) + P(T) - P(S∧T)
• Proofs for the properties are not given here and you can work them out by yourselves using Venn
Diagrams.
Conditional Property
• Conditional Property is defined as the probability of a given event given another event. It is
denoted by P(B|A) and is read as: ''Probability of B given probability of A.''
• Property 3: P(B|A) = P(B∧A) / P(A).
Bayes' Theorem
• Given P(A), P(B) and P(A|B), then
• P(B|A) = P(A|B) x P(B) / P(A)
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Bayesian Network
When designing a Bayesian Network, we keep
the local probability table at each node.
Bayesian Network - Example
Consider a Bayesian Network as given below:
This Bayesian Network tells us the reason a
particular person cannot study. It may be
either because of no electricity or because of
his lack of interest. The corresponding
probabilities are written in front of the causes.
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No Electricity Not interested P(Cannot Study)
F F
P(No electricity = F) x P(Not
Interested = F) = 0.8 x 0.7 =
0.56
F T
P(No electricity = F) x P(Not
Interested = T) = 0.8 x 0.3 =
0.24
T F
P(No electricity = T) x
P(Not Interested = F) = 0.2
x 0.7 = 0.14
T T
P(No electricity = T) x
P(Not Interested = T) = 0.2
x 0.3 = 0.06
Now, as you can see no cause
is dependent on each other
and they directly contribute
to the person's inability to
study. To plot the third table,
we consider four cases. Since,
the causes are independent,
their corresponding
probabilities can be
multiplied directly.
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The updated
Bayesian Network
is:
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Table of Contents
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Data Mining
• In artificial intelligence and machine learning, data mining, or knowledge discovery in databases, is the nontrivial
extraction of implicit, previously unknown and potentially useful information from data. Statistical methods are used that
enable trends and other relationships to be identified in large databases.
• The major reason that data mining has attracted attention is due to the wide availability of vast amounts of data, and the
need for turning such data into useful information and knowledge. The knowledge gained can be used for applications
ranging from risk monitoring, business management, production control, market analysis, engineering, and science
exploration.
In general, three types of data mining techniques are used: association, regression, and classification.
Association analysis
• Association analysis is the discovery of association rules showing attribute-value conditions that occur frequently together
in a given set of data. Association analysis is widely used to identify the correlation of individual products within shopping
carts.
Regression analysis
• Regression analysis creates models that explain dependent variables through the analysis of independent variables. As an
example, the prediction for a product’s sales performance can be created by correlating the product price and the average
customer income level.
Classification and prediction
• Classification is the process of designing a set of models to predict the class of objects whose class label is unknown. The
derived model may be represented in various forms, such as if-then rules, decision trees, or mathematical formulas.
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• A decision tree is a flow-chart-like tree structure where each node denotes a test on an attribute
value, each branch represents an outcome of the test, and each tree leaf represents a class or
class distribution. Decision trees can be converted to classification rules.
• Classification can be used for predicting the class label of data objects. Prediction encompasses
the identification of distribution trends based on the available data.
The data mining process consists of an iterative sequence of the following steps:
• Data coherence and cleaning to remove noise and inconsistent data
• Data integration such that multiple data sources may be combined
• Data selection where data relevant to the analysis are retrieved
• Data transformation where data are consolidated into forms appropriate for mining
• Pattern recognition and statistical techniques are applied to extract patterns
• Pattern evaluation to identify interesting patterns representing knowledge
• Visualization techniques are used to present mined knowledge to users
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Data Mining

Limits of Data Mining
• GIGO (garbage in garbage out) is almost always referenced with respect to data mining, as the
quality of the knowledge gained through data mining is dependent on the quality of the historical
data. We know data inconsistencies and dealing with multiple data sources represent large
problems in data management.
• Data cleaning techniques are used to deal with detecting and removing errors and inconsistencies
to improve data quality; however, detecting these inconsistencies is extremely difficult. How can
we identify a transaction that is incorrectly labeled as suspicious? Learning from incorrect data
leads to inaccurate models.
• Another limitation of data mining is that it only extracts knowledge limited to the specific set of
historical data, and answers can only be obtained and interpreted with regards to previous trends
learned from the data.
• This limits one’s ability to benefit from new trends. Because the decision tree is trained
specifically on the historical data set, it does not account for personalization within the
tree. Additionally, data mining (decision trees, rules, clusters) are non-incremental and do not
adapt while in production.
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Data Mining

Table of Contents
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196
Operation of Fuzzy System
Crisp Input
Fuzzy Input
Fuzzy Output
Crisp Output
Fuzzification
Rule Evaluation
Defuzzification
Input Membership Functions
Rules / Inferences
Output Membership Functions

197
Building Fuzzy Systems
⚫ Fuzzification
⚫ Inference
⚫ Composition
⚫ Defuzzification

198
Fuzzification
⚫ Establishes the fact base of the fuzzy system. It identifies the input and output of the
system, defines appropriate IF THEN rules, and uses raw data to derive a
membership function.
⚫ Consider an air conditioning system that determine the best circulation level by
sampling temperature and moisture levels. The inputs are the current temperature
and moisture level. The fuzzy system outputs the best air circulation level: “none”,
“low”, or “high”. The following fuzzy rules are used:
1. If the room is hot, circulate the air a lot.
2. If the room is cool, do not circulate the air.
3. If the room is cool and moist, circulate the air slightly.
⚫ A knowledge engineer determines membership functions that map temperatures
to fuzzy values and map moisture measurements to fuzzy values.

199
Inference
⚫ Evaluates all rules and determines their truth values. If an input does not
precisely correspond to an IF THEN rule, partial matching of the input data is
used to interpolate an answer.
⚫ Continuing the example, suppose that the system has measured temperature
and moisture levels and mapped them to the fuzzy values of .7 and .1
respectively. The system now infers the truth of each fuzzy rule.
⚫ To do this a simple method called MAX-MIN is used. This method sets the
fuzzy value of the THEN clause to the fuzzy value of the IF clause. Thus, the
method infers fuzzy values of 0.7, 0.1, and 0.1 for rules 1, 2, and 3
respectively.

200
Composition
⚫ Combines all fuzzy conclusions obtained by inference into a single conclusion.
Since different fuzzy rules might have different conclusions, consider all rules.
⚫ Continuing the example, each inference suggests a different action
⚫ rule 1 suggests a "high" circulation level
⚫ rule 2 suggests turning off air circulation
⚫ rule 3 suggests a "low" circulation level.
⚫ A simple MAX-MIN method of selection is used where the maximum fuzzy value
of the inferences is used as the final conclusion. So, composition selects a fuzzy
value of 0.7 since this was the highest fuzzy value associated with the inference
conclusions.

201
Defuzzification
⚫ Convert the fuzzy value obtained from composition into a “crisp” value. This
process is often complex since the fuzzy set might not translate directly into a
crisp value.Defuzzification is necessary, since controllers of physical systems
require discrete signals.
⚫ Continuing the example, composition outputs a fuzzy value of 0.7. This
imprecise value is not directly useful since the air circulation levels are “none”,
“low”, and “high”. The defuzzification process converts the fuzzy output of
0.7 into one of the air circulation levels. In this case it is clear that a fuzzy
output of 0.7 indicates that the circulation should be set to “high”.

202
Defuzzification
⚫ There are many defuzzification methods. Two of the more common
techniques are the centroid and maximum methods.
⚫ In the centroid method, the crisp value of the output variable is
computed by finding the variable value of the center of gravity of the
membership function for the fuzzy value.
⚫ In the maximum method, one of the variable values at which the fuzzy
subset has its maximum truth value is chosen as the crisp value for the
output variable.

Example: Design of Fuzzy Expert System – Washing
Machine
FDP on AI & Advanced Machine Learning using Data
Science, 22/11/19
203

Fuzzification
Given inputs x1 and x2, find the weight
values associated with each input
membership function.
Z
NM NS PS PM
X1
0.2
0.7
W = [0, 0, 0.2, 0.7, 0]

Fuzzy Rules
Demystifying AI algorithms
Not Greasy Medium Greasy
Small Dirt Time= Vshort Medium Long
Medium Dirt Short Medium Long
Large Dirt Medium Long Very Long

DeFuzzification
Medium
Short Long
30
Demystifying AI algorithms
10 20 40
(Y – 20)/(30-20) = 0.5
Y – 20 = 0.5* 10 = 5
Y = 25 Mins
Washing Time Long = (Y- 30)/(40-30)
Washing Time Medium = (Y- 20)/(30-20)
5
Very short Very Long
60
X1 and X2 = 0.5

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network
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Dempster Shafer Theory
• Dempster Shafer Theory is given by Arthure P.Dempster in 1967 and his student
Glenn Shafer in 1976.
This theory is being released because of following reason:-
• Bayesian theory is only concerned about single evidences.
• Bayesian probability cannot describe ignorance.
• DST is an evidence theory, it combines all possible outcomes of the problem.
Hence it is used to solve problems where there may be a chance that a different
evidence will lead to some different result.
The uncertainity in this model is given by:-
• Consider all possible outcomes.
• Belief will lead to believe in some possibility by bringing out some evidence.
• Plausibility will make evidence compatibility with possible outcomes.
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For eg:-
let us consider a room where four person are presented A, B, C, D(lets say) And suddenly lights out and
when the lights come back B has been died due to stabbing in his back with the help of a knife. No one came
into the room and no one has leaved the room and B has not committed suicide. Then we have to find out
who is the murdrer?
To solve these there are the following possibilities:
• Either {A} or{C} or {D} has killed him.
• Either {A, C} or {C, D} or {A, C} have killed him.
• Or the three of them kill him i.e; {A, C, D}
• None of the kill him {o}(let us say).
These will be the possible evidences by which we can find the murderer by measure of plausibIlity.
Using the above example we can say :
Set of possible conclusion (P): {p1, p2….pn}
where P is set of possible conclusion and cannot be exhaustive means at least one (p)i must be true.
(p)i must be mutually exclusive.
Power Set will contain 2n elements where n is number of elements in the possible set.
For eg:-
If P = { a, b, c}, then Power set is given as
{o, {a}, {b}, {c}, {a, b}, {b, c}, {a, c}, {a, b, c}}= 23 elements.
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• Mass function m(K): It is an interpretation of m({K or B}) i.e; it means there is
evidence for {K or B} which cannot be divided among more specific beliefs for K
and B.
• Belief in K: The belief in element K of Power Set is the sum of masses of element
which are subsets of K. This can be explained through an example
Lets say K = {a, b, c}
Bel(K) = m(a) + m(b) + m(c) + m(a, b) + m(a, c) + m(b, c) + m(a, b, c)
• Plaausiblity in K: It is the sum of masses of set that intersects with K.
i.e; Pl(K) = m(a) + m(b) + m(c) + m(a, b) + m(b, c) + m(a, c) + m(a, b, c)
Characteristics of Dempster Shafer Theory:
• It will ignorance part such that probability of all events aggregate to 1.
• Ignorance is reduced in this theory by adding more and more evidences.
• Combination rule is used to combine various types of possibIlities.
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Advantages:
• As we add more information, uncertainty interval reduces.
• DST has much lower level of ignorance.
• Diagnose Hierarchies can be represented using this.
• Person dealing with such problems is free to think about evidences.
Disadvantages:
• In this computation effort is high, as we have to deal with 2n of sets.
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Example: 4 people (B, J, S and K) are locked in a room when light goes out .
When light comes on, K is dead, staffed whit a knife.
Not suicide (staffed in the back)
No one entered room.
Assume only one killer
P(ϴ) = ({
Detectives after receiving the crime scene, assign mass probabilities to various elements of the
power set:
Event Mass
No one is guilty 0
B is guilty 0.1
J is guilty 0.2
S is guilty 0.1
Either B or J is guilty 0.1
Either B or S is guilty 0.1
Either S or J is guilty 0.3
One of the 3 is guilty 0.1
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Dempster Shafer Problem
•P(
) = ({

Belief in A:
The belief in an element A of the power set is the sum of the masses of
elements which are subsets of A (including A itself)
Ex: Given A= {q1, q2, q3}
Bet (A)
={m(q1)+m(q2)+m(q3)+m(q1,q2)+m(q2,q3),m(q1,q3)+m(q1,q2,q3)}
Ex: Given the above mass assignments,
Bel(B) = m(B) =0.1
Bel (B,J) = m(B)+m(J)+m(B,J) = 0.1+0.2=0.1 0.4
RESULT:
•
•
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A {B} {J} {S} {B,J} {B,S} {S,J} {B,J,S}
M(A) 0.1 0.2 0.1 0.1 0.1 0.3 0.1
Bel (A) 0.1 0.2 0.1 0.4 0.3 0.6 1.0

Plausibility of A: pl(A)
The plausibility of an element A, pl(a), is the sum of all the masses of
the sets that instruct with the
Set A : Ex:Pl (B,J) =M(B) +m(J)+M(B,J)+M(B,S)+M(J,S)+M(B,J,S)=0.9
All Result:
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A {B} {J} {S} {B,J} {B,S} {S,J} {B,J,S}
M(A) 0.1 0.2 0.1 0.1 0.1 0.3 0.1
Pl (A) 0.4 0.7 0.6 0.9 0.8 0.9 1.0

Disbelief (or Doubt) in A: dis (A)
The disbelief in A is simply bel (7A)
It is calculated by summing all masses of elements which do not intersect with A
D is (A) = 1-pl (A)
Or
Pl (A) = 1-Dis (A)
Belief Interval of A:
The certainty associated with a give subset A is defined by the belief interval: [bel(A) p(A)]
Ex . The belief interval of (B,S) IS [0.3,08]
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A {B} {J} {S} {B,J} {B,S} {S,J} {B,J,S}
M(A) 0.1 0.2 0.1 0.1 0.1 0.3 0.1
Bel (A) 0.1 0.2 0.1 0.4 0.3 0.6 1.0
Pl(A) 0.4 0.7 0.6 0.9 0.8 0.9 1.0
A {B} {J} {S} {B,J} {B,S} {S,J} {B,J,S}
Pl(A) 0.4 0.7 0.6 0.9 0.8 0.9 1.0
Dis(A) 0.6 0.3 0.4 0.1 0.2 0.1 0.0

P(A) represents the maximum share of the evidence. We could possibly have, if for all its that intouects with A, the part that intracts actually
valid. So, Pl(A) is the max possible value of prof(A).
Belief intervals and Probability
The probability in A falls some ware between bel (A) and pl(A).
-bel (A) represents the evidence. We have for a directly So proof (A) cannot be less than this value.
- PL(A) represents the maximum share of the evidence we could possibly have. If, for all sets that intersects with A, the part that intersects is
actually valid. So, PL(A) is the max possible value of proof(A).
Belief intervals allow Dempster, Shaffer theory to reason about the degree of certainity or certainity of our beliefs.
A small difference between belief and plausibility shows that we are curtain about our belief.
A large difference shows that we are uncertain about our belief.
however, even with a ‘O’ interval, this does not mean we know which conclusion is right.
17-03-2021 18CSC305J_AI_UNIT3 216
A {B} {J} {S} {B,J} {B,S} {S,J} {B,J,S}
M(A) 0.1 0.2 0.1 0.1 0.1 0.3 0.1
Bel (A) 0.1 0.2 0.1 0.4 0.3 0.6 1.0
Pl(A) 0.4 0.7 0.6 0.9 0.8 0.9 1.0
Belief
interval
{0.1,0.4} {0.2,0.7} {0.1,0.6} {0.4,0.9} {0.3,0.8} {0.6,0.9} {1,1}

Thank You
17-03-2021 18CSC305J_AI_UNIT3 217

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