Unit 2 ai

Unit 2
An Introduction to Artificial
Intelligence

What is intelligence?
– the ability to reason
– the ability to understand
– the ability to create
– the ability to Learn from experience
– the ability to plan and execute complex tasks
"Giving machines ability to perform tasks
normally associated with human intelligence."

• According to Barr and Feigenbaum:
• “Artificial Intelligence is the part of computer
science concerned with designing intelligence
computer systems, that is, systems that exhibit
the characteristics we associate with
intelligence in human behavior.”

Systems that think like humans
“The exciting new effort to make
computers think…..machine with minds,
in the full and literal sense.” (Haugeland,
1985)
“[The automaton of] activities that we
associate with human thinking, activities
such as decision-making, problem
solving, learning…..” (Bellman, 1978)
Systems that think rationally
“The study of mental faculties through
the use of computational models.”
(Charniak and McDermott, 1985)
“The study of the computations that
make it possible to perceive, reason, and
act.” (Winston, 1992)
Systems that act like humans
“ The art of creating machines that
perform functions that require intelligence
when performed by people.” (Kurzweil,
1990)
“The study of how to make computer do
things at which, at the moment, people
are better.” (Rich and Knight, 1991)
Systems that act rationally
“Computational Intelligence is the study
of the design of intelligent agents.” (Poole
et al., 1998)
“AI… is concerned with intelligent behavior
in artifacts.” (Nilsson, 1998)

What is AI
• Artificial Intelligence, or AI, is the attempt to
build intelligent computer systems, that is, to
make computer systems that are similar to the
human mind in certain aspect.
• So, what is Intelligence?
• “the capacity to learn and solve problems” -
Webster’s dictionary

What is AI?
• There is no widely accepted definition of
Artificial Intelligence, therefore all AI definitions
are grouped into four categories:
• Systems that think like humans (e.g. Cognitive
Modeling)
• Systems that act like humans (e.g. Turing's test)
• Systems that think rationally (e.g. Deep Blue)
• Systems that act rationally (e.g. Robots)

Thinking like a human
• Figure out how we think by introspection or
scientific experimentation and express the
theory for computer.
• Machine operates as human does.
“machine with minds”

Turing Test
• Proposed by Allan Turing in 1950, measure the performance
of intelligent machine against that of human being.
• The computer posses the test if a human interrogator, after
posing some written questions, cannot tell whether the
written responses come from a person or not.
• “The art of creating machines that performs functions that
require intelligence when performed by people” -Kurzweil

Turing Test
• For this, machine should be capable of
• To pass a Turing test, a computer must have following
capabilities:
• Natural Language Processing: Must be able to communicate
successfully in English
• Knowledge representation: To store what it knows and
hears.
• Automated reasoning: Answer the Questions based on the
stored information.
• Machine learning: Must be able to adapt in new
circumstances.

• Advantages:
–Provides the standard for determining intelligence.
–Eliminates any bias in favor of living organism.
• Problem:
–Turing test is not reproducible, constructive, or
amenable to mathematical analysis
–Excludes physical aspect.
• More satisfactory operational definition of AI

Thinking Rationally
• Rational: right decision based on given knowledge.
• Given knowledge:
– “Socrates is a man; all men are mortal; therefore Socrates is mortal.”
• Derive conclusion through logic:
• Many mathematician derive logical notation and rules of derivation for
thoughts.
• Problems:
–It is not easy to take informal knowledge and state in the formal notation.
–How much fact can store and how fast machine can process these facts?
• “Sometimes quantity becomes quality” –Garry Kasparov (World Chess
Champion 1997)

Acting Rationally
• The rational agent: perform actions which will (most likely)
achieve one's goals.
• Knowledge may not be perfect all the times, we need to go
beyond strict rational thought in general.
–For example: recoiling from a hot stove must be a fast
action than a slower action taken after careful deliberation.
• Advantages:
• More general than lows of thought approach: correct
inference is just one of the several possible mechanisms for
achieving rationality.

State of the Art
What can AI do Today?
Can we build Intelligent Hardware?
How complicated is our brain?
–a neuron, or nerve cell, is the basic information processing unit
–estimated to be on the order of 10^12 neurons in a human brain
–many more synapses (10^14) connecting these neurons
How complex can we make computers?
–10^6 or more transistors per CPU
–supercomputer: hundreds of CPUs, 10^9 bits of RAM
Conclusion
YES: in the near future we can have computers with as many basic processing
elements as our brain, but with
–far fewer interconnections (wires or synapses) than the brain.
–much faster updates than the brain. but building hardware is very different
from making a computer behave like a brain!

Can Computer be foolproof
(error free)?
• An intelligent system can make errors and still be
intelligent
–humans are not right all of the time
–we learn and adapt from making mistakes
• e.g., consider learning to ski
–we improve by taking risks and falling
–an intelligent system can learn in the same way
• Conclusion
• NO: intelligent systems will not (and need not) be
foolproof. System learn from the errors

• Could Computer beat human at chess?
• –YES: today’s computers (Deep Blue) can beat even the best
human (Gerry Kasparov, World Champion).
• Could Computer talk and sound like a human?
–NO, for complete sentences, but YES for individual words.
• Could Computer recognize human speech?
–NO, normal speech is too complex to accurately recognize, but
YES for restricted problems.
• (e.g., recent software for PC use by IBM, Dragon systems, etc)

Could Computer understand what we are saying?
Understanding is different to recognition:
“Time flies like an arrow”
• assume the computer can recognize all the words
• but how could it understand it?
1. time passes quickly like an arrow?
2. time flies the way an arrow flies
3. only time those flies which are like an arrow
4. “time-flies” are fond of arrows
• only 1. makes any sense, but how could a computer figure this out?
• clearly humans use a lot of implicit commonsense knowledge in
communication
Conclusion: NO, much of what we say is beyond the capabilities of a
computer to understand at present.

• Could Computer learn to adapt?
• YES, computers can learn and adapt, when presented
with information in the appropriate way.
• Could Computer see?
• mostly NO: computers can only “see” certain types of
objects under limited circumstances: but
• YES for certain constrained problems (e.g., face
recognition).
• Could Computer plan and make decisions?
• NO, real-world planning and decision-making is still
beyond the capabilities of modern computers
• exception: very well-defined, constrained problems

Assignment 2
• History of AI

Applications of AI
• Autonomous planning and scheduling
• Game playing
• Autonomous Control
• Diagnosis
• Logistics Planning
• Robotics
• Language understanding and problem solving

Intelligent Agents
• An Intelligent Agent perceives its environment
via sensors and acts rationally upon that
environment with its effectors (actuators).
Hence, an agent gets percepts one at a time,
and maps this percept sequence to actions.

Properties of the agent
– Autonomous
– Interacts with other agents plus the
environment
– Reactive to the environment
– Pro-active (goal- directed)

What do you mean, sensors/percepts
and effectors/actions?
• For Humans
– Sensors: Eyes (vision), ears (hearing), skin (touch), tongue
(gestation), nose, neuromuscular system
– Percepts:
• After preprocessing – objects in the visual field (location, textures,
colors, …), auditory streams (pitch, loudness, direction), …
– Effectors: limbs, digits, eyes, tongue, …..
– Actions: lift a finger, turn left, walk, run, carry an object, …
The Point: percepts and actions need to be carefully defined,
possibly at different levels of abstraction

A more specific example: Automated
taxi driving system
• Percepts: Video, sonar, speedometer, odometer,
engine sensors, keyboard input, microphone,
GPS, …
• Actions: Steer, accelerate, brake, horn,
speak/display, …
• Goals: Maintain safety, reach destination,
maximize profits (fuel, tire wear), obey laws,
provide passenger comfort, …
• Environment: Urban streets, freeways, traffic,
pedestrians, weather, customers, …

• Percept: The Agents perceptual inputs at any given
instant.
• Percept Sequence: The complete history of
everything the agent has ever perceived.
• The agent function is mathematical concept that maps
percept sequence to actions.
f : P* -> A
• The agent function will internally be represented by the
agent program.
• The agent program is concrete implementation of agent
function it runs on the physical architecture to produce
f.

The vacuum-cleaner world:
Example of Agent
• Environment: square A and B
• Percepts: [location and
content] E.g. [A, Dirty]
• Actions: left, right, suck, and no-op
Percept sequence
[A, Clean]
[A, Dirty]
[B, Clean]
[B, Dirty]
……….
Action
Right
Suck
Left
Suck
……

The concept of rationality
• A rational agent is one that does the right thing.
• –Every entry in the table is filled out correctly.
• What is the right thing?
• –Right action is the one that will cause the agent to be most
successful.
• Therefore we need some way to measure success of an agent.
Performance measures are the criterion for success of an agent
behavior.
• E.g., performance measure of a vacuum-cleaner agent could be
amount of dirt cleaned up, amount of time taken, amount of
electricity consumed, amount of noise generated, etc.
• It is better to design Performance measure according to what is
wanted in the environment instead of how the agents should behave.

What is rational at a given time
depends on four things:
• Performance measure,
• Prior environment knowledge,
• Actions,
• Percept sequence to date (sensors).
• Definition: A rational agent chooses whichever
action maximizes the expected value of the
performance measure given the percept
sequence to date and prior environment
knowledge.

Environments
• To design a rational agent we must specify its
task environment. Task environment means:
PEAS description of the environment:
– Performance
– Environment
– Actuators
– Sensors

Example: Fully automated taxi:
• PEAS description of the environment:
• Performance: Safety, destination, profits,
legality, comfort
• Environment: Streets/freeways, other traffic,
pedestrians, weather,, …
• Actuators: Steering, accelerating, brake, horn,
speaker/display,…
• Sensors: Video, sonar, speedometer, engine
sensors, keyboard, GPS, …

Knowledge Representation
• Knowledge
• Knowledge is a theoretical or practical understanding of a subject or a
domain. Knowledge is also the sum of what is currently known.
• Knowledge is “the sum of what is known: the body of truth, information,
and principles acquired by mankind.” Or, "Knowledge is what I know,
Information is what we know."
• There are many other definitions such as:
- Knowledge is "information combined with experience, context,
interpretation, and reflection. It is a high-value form of information that is
ready to apply to decisions and actions." (T. Davenport et al., 1998)
- Knowledge is “human expertise stored in a person’s mind, gained through
experience, and interaction with the person’s environment." (Sunasee and
Sewery, 2002)
- Knowledge is “information evaluated and organized by the human mind
so that it can be used purposefully, e.g., conclusions or explanations."
(Rousa, 2002)

• Knowledge consists of information that has been:
• interpreted,
• categorized,
• applied,
• experienced
• and revised
• In general, knowledge is more than just data, it consist
of: facts, ideas, beliefs, heuristics, associations, rules,
abstractions, relationships, customs.

Research literature classifies
knowledge as follows
• Classification-based Knowledge » Ability to
classify information
• Decision-oriented Knowledge »Choosing the best
option
• Descriptive knowledge »State of some world
(heuristic)
• Procedural knowledge »How to do something
• Reasoning knowledge »What conclusion is valid
in what situation?
• Assimilative knowledge »What its impact is?

Knowledge Representation
• Knowledge representation (KR) is the study of how
knowledge about the world can be represented and
what kinds of reasoning can be done with that
knowledge. Knowledge Representation is the method
used to encode knowledge in Intelligent Systems.
• Since knowledge is used to achieve intelligent behavior,
the fundamental goal of knowledge representation is to
represent knowledge in a manner as to facilitate
inferencing (i.e. drawing conclusions) from knowledge.
A successful representation of some knowledge must,
then, be in a form that is understandable by humans,
and must cause the system using the knowledge to
behave as if it knows it.

Some issues that arise in knowledge representation
from an AI perspective are:
• How do people represent knowledge?
• What is the nature of knowledge and how do we
represent it?
• Should a representation scheme deal with a
particular domain or should it be general purpose?
• How expressive is a representation scheme or
formal language?
• Should the scheme be declarative or procedural?

• Fig: Two entities in Knowledge Representation
• For example: English or natural language is an obvious way of representing
and handling facts.
• Logic enables us to consider the following fact:
spot is a dog as
dog(spot) We could then infer that all dogs have tails with:
: dog(x)
hasatail(x) We can then deduce:
hasatail(Spot)
• Using an appropriate backward mapping function the English sentence
Spot has a tail can be generated.

Properties for Knowledge
Representation Systems
• The following properties should be possessed by a knowledge
representation system.
• Representational Adequacy
- the ability to represent the required knowledge;
• Inferential Adequacy
- the ability to manipulate the knowledge represented to
produce new knowledge corresponding to that inferred from the
original;
• Inferential Efficiency
- the ability to direct the inferential mechanisms into the most
productive directions by storing appropriate guides;
• Acquisitional Efficiency
- the ability to acquire new knowledge using automatic methods
wherever possible rather than reliance on human intervention.

Approaches to Knowledge
Representation
• Rule-based
– IF <condition> THEN <conclusion>
• Object - based
– Frames
– Scripts
– Semantic Networks
– Object – Attribute – Value(O – A -V Triplets)
• Example-based : Case based Reasoning (CBR)
• These methods are interrelated and may be utilized in
combination or individually within an expert system or
other AI structure

Rule based approach:
• Rule-based systems are used as a way to store and
manipulate knowledge to interpret information in
a useful way. In this approach, idea is to use
production rules, sometimes called IF-THEN
rules. The syntax structure is
• IF <premise> THEN <action>
• <premise> - is Boolean. The AND, and to a lesser
degree OR and NOT, logical connectives are
possible.
• <action> - a series of statements

A typical rule-based system has four
basic components:
• A list of rules or rule base, which is a specific
type of knowledge base.
• An inference engine or semantic reasoner,
which infers information or takes action based
on the interaction of input and the rule base.
• Temporary working memory.
• A user interface or other connection to the
outside world through which input and output
signals are received and sent.

• Working Memory contains facts about the world and can be observed
directly or derived from a rule. It contains temporary knowledge –
knowledge about this problem-solving session. It may be modified by
the rules.
• It is traditionally stored as <object, attribute, value> triplet.
• Rule Base contains rules; each rule is a step in a problem solving
process. Rules are persistent knowledge about the domain. The rules
are typically only modified from the outside of the system, e.g. by an
expert on the domain.
• The syntax isa IF <conditions> THEN <actions> format.
• The conditions are matched to the working memory, and if they are
fulfilled, the rule may be fired.
• Actions can be:
• Adding fact(s) to the working memory.
• Removing fact(s) from the working memory
• Modifying fact(s) in the working memory.

Advantages of rule based approach:
• - Naturalness of Expression: Expert knowledge can often been seen naturally as
rules of thumb.
• - Modularity: Rules are independent of each other – new rules can be added or
revised later. Interpreter is independent from rules.
• - Restricted Syntax: Allows construction of rules and consistency checking by other
programs. Allows (fairly easy) rephrasing to natural language.
• Disadvantages (or limitations)
• - rule bases can be very large (thousands of rules)
• - rules may not reflect the actual decision making
• - the only structure in the KB is through the rule chaining
• Examples: “If the patient has stiff neck, high fever and an headache, check for
Brain Meningitis”. Then it can be represented in rule based approach as:
• IF <FEVER, OVER, 39> AND <NECK, STIFF, YES> AND <HEAD, PAIN, YES>
THEN
• add(<PATIENT,DIAGNOSE, MENINGITIS>)

Object-based Approach: Frames
• With this approach, knowledge may be represented in a data structure called a frame. A frame is a data structure containing typical knowledge
about a concept or object (Marvin Minsky (mid 1970s)). A frame represents knowledge about real world things (or entities).
• Each frame has a name and slots. Slots are the properties of the entity that has the name, and they have values or pointer to other frames ( a
table like data structure). A particular value may be:
• -
• a default value
• -
• an inherited value from a higher frame
• -
• a procedure, called a daemon, to find a value
• - a specific value, which might represent an exception.
• When the slots of a frame are all filled, the frame is said to be instantiated, it now represents a specific entity of the type defined by the unfilled
frame. Empty frames are sometimes called object prototypes
• The idea of frame hierarchies is very similar to the idea of class hierarchies found in object- orientated programming. F
• rames are an application of the
• object
• -
• oriented
• approach to
• knowledge
• -
• based systems
• .

• Disadvantages
• - complex
• - reasoning (inferencing) is difficult
• - explanation is difficult, expressive limitation
• Advantages
• - knowledge domain can be naturally structured
[a similar motivation as for the O-O approach].
• - easy to include the idea of default values, detect
missing values, include specialised procedures
and to add further slots to the frames

Object-based Approach: Semantic Network
• Semantic Networks and Frames are called Network Representations or Associative Representations
• Intuition: Knowledge is not a large collection of small pieces of knowledge but larger pieces that are highly
interconnected. Logics and Rule-Based Systems seem to not have this property.
• The meaning of concepts emerges from how it is connected to other concepts.
• Semantic networks can
• - show natural relationships between objects/concepts
• - be used to represent declarative/descriptive knowledge
• Knowledge is represented as a collection of concepts, represented by nodes. Thus, s
• emantic
• networks are constructed using
• nodes
• linked by directional lines called
• arcs
• A node can represent a fact description
• - physical object
• - concept
• - event
• An arc (or link) represents relationships between nodes. There are some 'standard' relationship types
• - 'Is-a' (instance relationship): represent class/instance relationships
• - 'Has-a' (part-subpart relationship): identify property relationships

• Semantic networks are mainly used as an aid to
analysis to visually represent parts of the problem
domain.
• One feature of a semantic net is the ability to use the
net to deduce new facts
• - Begin with the fact that - all robins are birds
• - If Clyde is the name of a particular pet robin then
• - By following the is-a links it is easy to deduce that
• Clyde is a bird

• Disadvantages of a semantic network
• - incomplete (no explicit operational/procedural
knowledge)
• - no interpretation standard
• - lack of standards, ambiguity in node/link descriptions
• - not temporal (i.e. doesn't represent time or sequence)
• Advantages of a semantic network
• - Explicit and easy to understand.
• - The net is its own index – quick inference possible.
• - Supports default reasoning in finite time.
• - Focus on bigger units of knowledge and
interconnectedness.

O-A-V Triple:
• Object – Attribute – Value (OAV) provides a particularly convenient
way in which to represent certain facts and heuristic rules within
KB. Each OAV triplet is present with specific entity, or object and a
set of attributes with their values associated to every object.
• Logic:
• Logic is a formal language for representing knowledge such that
conclusions can be drawn. Logic makes statements about the
world which are true (or false) if the state of affairs it represents is
the case (or not the case). Compared to natural languages
(expressive but context sensitive) and programming languages
(good for concrete data structures but not expressive) logic
combines the advantages of natural languages and formal
languages. Logic is concise, unambiguous, expressive, context
insensitive, effective for inferences.

• It has syntax, semantics, and proof theory.
• Syntax: Describe possible configurations that constitute sentences.
• Semantics: Determines what fact in the world, the sentence refers
to i.e. the interpretation. Each sentence make claim about the
world (meaning of sentence).Semantic property include truth and
falsity.
• Proof theory(Inference method): set of rules for generating new
sentences that are necessarily true given that the old sentences
are true.
• We will consider two kinds of logic: propositional logic and first-
order logic or more precisely first-order predicate calculus.
Propositional logic is of limited expressiveness but is useful to
introduce many of the concepts of logic's syntax, semantics and
inference procedures.

• Entailment:
• Entailment means that one thing follows from another:
• KB |= α
• Knowledge base KB entails sentence α if and only if α is
true in all worlds where KB is true
• E.g., x + y =4 entails 4=x + y
• Entailment is a relationship between sentences (i.e.,
syntax) that is based on semantics.
• We can determine whether S |= P by finding Truth
Table for S and P, if any row of Truth Table where all
formulae in S is true.

Example:
Therefore {P, P→Q} |= Q. Here, only row where both P and P→Q
are True, Q is also True. Here, S= (P, P→Q} and P= {Q}.
Models
Logicians typically think in terms of models, in place of “possible
world”, which are formally structured worlds with respect to
which truth can be evaluated.
m is a model of a sentence if is true in m.
M( ) is the set of all models of .

Propositional Logic:
• Propositional logic represents knowledge/ information
in terms of propositions. Prepositions are facts and
non-facts that can be true or false. Propositions are
expressed using ordinary declarative sentences.
Propositional logic is the simplest logic.
• Syntax:
• The syntax of propositional logic defines the allowable
sentences. The atomic sentences- the indivisible
syntactic elements- consist of single proposition
symbol. Each such symbol stands for a proposition that
can be true or false. We use the symbols like P1, P2 to
represent sentences.

• The complex sentences are constructed from simpler sentences
using logical connectives. There are five connectives in common
use:
• (negation), ^ (conjunction), (disjunction), (implication),
(biconditional)
• The order of precedence in propositional logic is from (highest to
lowest): , ^ , , , .
• Propositional logic is defined as:
• If S is a sentence, S is a sentence (negation)
• If S1 and S2 are sentences, S1 ^ S2 is a sentence (conjunction)
• If S1 and S2 are sentences, S1 S2 is a sentence (disjunction)
• If S1 and S2 are sentences, S1 S2 is a sentence (implication)
• If S1 and S2 are sentences, S1 S2 is a sentence (biconditional)

Semantics:
• Each model specifies true/false for each proposition
symbol
• Rules for evaluating truth with respect to a model:
• S is true if, S is false
• S1 ^ S2 is true if, S1 is true and S2 is true
• S1 S2 is true if, S1 is true or S2 is true
• S1 S2 is true if, S1 is false or S2 is true
• S1 S2 is true if, S1 S2 is true and S2 S1 is true
• Truth Table showing the evaluation of semantics of
complex sentences:

Logical equivalence:
• Two sentences and ß are logically equivalent
( ß) iff true they are true inn same set of
models or Two sentences and ß are logically
equivalent ( ß) iff |= ß and ß |= .

• Validity:
• A sentence is valid if it is true in all models,
• e.g., True, A A, A A, (A (A B)) B
• Valid sentences are also known as tautologies. Every valid sentence is logically
equivalent to True
• Satisfiability:
• A sentence is satisfiable if it is true in some model
• – e.g., A B, C
• A sentence is unsatisfiable if it is true in no models
• – e.g., A A
• Validity and satisfiablity are related concepts
• – is valid iff is unsatisfiable
• – is satisfiable iff is not valid
• Satisfiability is connected to inference via the following:
• – KB |= if and only if (KB ) is unsatisfiable

Unit 2 ai

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Unit 2 ai