Artificial Intelligence Notes Unit 1

UNIT 1
GENERAL ISSUES AND OVERVIEW OF AI
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Prepared by : -Agniwesh Mishra, RCET,
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Books
• Text Books
1. Elaine Rich and Kevin Knight: Artificial Intelligence- Tata McGraw Hill.
2. Dan W. Patterson, Introduction to Artificial Intelligence and Expert
Systems- Prentice Hall of India.
• Reference Books
1. Nils J. Nilsson: Principles of Artificial Intelligence- Narosa Publishing
house.
2. Artificial Intelligence : A Modern Approach, Stuart Rusell, Peter
Norvig, Pearson Education, 2nd Edition
3. Artificial Intelligence, Winston, Patrick, Henry, Pearson Education
4. Artificial Intelligence and Expert System, Gopal Krishna , Janakiraman
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Syllabus
• Introduction to AI,
• Problem Solving, State space search,
• Blind search:
 Depth first search,
 Breadth first search,
• Informed search:
 Heuristic function,
 Hill climbing search,
 Best first search,
 A* & AO* Search,
 Constraint satisfaction.
• Game tree
• Evaluation function,
• Mini-Max search,
• Alpha-beta pruning,
• Games of chance.
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Introduction to AI
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What is AI ?
• Artificial Intelligence is the science and engineering of making
intelligent machines.
• Artificial Intelligence is the study of how to make computers
do things which, at the moment, people do better.
• Artificial Intelligence is the branch of computer science that is
concerned with the automation of intelligent behavior.
• Artificial Intelligence is the study and design of intelligent
agents, where an intelligent agent is a system that perceives its
environment and takes actions that maximize its chance of
success.
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What is AI ?
• Artificial Intelligence is concerned with the design of
intelligence in an artificial device.
• The term was coined by McCarthy in 1956.
• There are two ideas in the definition.
– Intelligence
– Artificial
• The term artificial is easy to understand. But it’s very difficult
to define intelligence.
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What is Intelligence?
• Intelligence is what we use when we don’t know what to do.
• Intelligence relates to tasks involving higher mental
processes.
Examples: Creativity, Solving problems, Pattern recognition,
Classification, Learning, Induction, Deduction, Building
analogies, Optimization, Language processing, Knowledge
and many more.
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Approaches to AI
• Hard or Strong AI
• Soft or Weak AI
• Applied AI
• Cognitive AI
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Hard or Strong AI
• Strong AI refers to a machine that approaches or supersedes
human intelligence.
– if it can do typically human tasks,
– If it can apply a wide range of background knowledge and
– If it has some degree of self-consciousness
• Strong AI aims to build machines whose overall intellectual
ability is indistinguishable from that of a human being.
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Soft or Weak AI
• Weak AI refers to the use of software to study or accomplish
specific problem solving or reasoning tasks that do not
encompass the full range of human cognitive abilities.
Example: a chess program such as Deep Blue
• Weak AI does not achieve self-awareness; it demonstrates
wide range of human level cognitive abilities; it is merely an
intelligent, a specific problem-solver.
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Applied AI
• Aims to produce commercially viable "smart"
systems such as, for example, a security
system that is able to recognize the faces of
people who are permitted to enter a
particular building.
• Applied AI has already enjoyed considerable
success.
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Cognitive AI
• Computers are used to test theories about
how the human mind works--for example,
theories about how we recognize faces and
other objects, or about how we solve abstract
problems.
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Cognitive science
• Aims to develop, explore and evaluate theories of how the
mind works through the use of computational models.
• The important is not what is done but how it is done; means
intelligent behavior is not enough, the program must operate in
an intelligent manner.
• Example: the chess programs are successful, but say little
about the ways humans play chess.
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Cybernetics
• Cybernetics” comes from a Greek word meaning “the art of
steering”.
• Cybernetics is about having and taking action to achieve that
goal. Knowing whether you have reached your goal (or at least
are getting closer to it) requires “”, a concept that comes from
cybernetics.
• Cybernetics grew from a desire to understand and build
systems that can achieve goals, whether complex human goals
or just goals like maintaining the temperature of a room under
changing conditions.
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Goals of AI
• The definition of AI gives four possible goals to pursue:
1. Systems that think like humans
2. Systems that think rationally
3. Systems that act like humans
4. Systems that act rationally
• Traditionally, all four goals have been followed and the approaches
were:
• Most of AI works falls into category 2 and 4.
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Systems that think like humans
• Most of the time it is a black box where we are not
clear about our thought process.
• One has to know functioning of brain and its
mechanism for possessing information.
• It is an area of cognitive science.
– The stimuli are converted into mental representation.
– Cognitive processes manipulate representation to build new
representations that are used to generate actions.
• Neural network is a computing model for processing
information similar to brain.
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Systems that act like humans
• The overall behaviour of the system
should be human like.
• It could be achieved by observation.
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Systems that think rationally
• Such systems rely on logic rather than human to
measure correctness.
• For thinking rationally or logically, logic formulas and
theories are used for synthesizing outcomes.
• For example,
– given John is a human and all humans are mortal then one
can conclude logically that John is mortal
• Not all intelligent behavior are mediated by logical
deliberation.
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Systems that act rationally
• Rational behavior means doing right thing.
• Goal is to develop systems that are rational
and sufficient.
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General AI Goals
• Replicate human intelligence
• Solve knowledge intensive tasks
• Make an intelligent connection between perception and action
• Enhance human-human, human-computer and computer to
computer interaction/communication
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AI Goal
Engineering based AI Goal
• Develop concepts, theory and practice of building intelligent
machines
• Emphasis is on system building
Science based AI Goal
• Develop concepts, mechanisms and vocabulary to understand
biological intelligent behavior.
• Emphasis is on understanding intelligent behavior.
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Major components of an AI system
AI Program
Knwoledge
Representation
Heuristic
Search
AI
Programming
languages and
tools
AI Hardware
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Major components of an AI system
• The quality of the result depends on how much knowledge the system
possesses. The available knowledge must be represented in a very
efficient way. Hence, knowledge representation is vital component of the
system.
• It is not merely enough that knowledge is represented efficiently. The
inference process should also be equally good for satisfactory results. The
inference process is broadly divided into brute and heuristic search
procedure.
• Today, just like we have specialized languages and programs for data
processing and scientific applications, we encounter specialized languages
and tools for AI programming. AI languages provide the basic functions for
AI programming and tools for the right environment.
• Today, most of the AI programs in India are implemented on Von
Neumann machines only. However dedicated workshops have emerged
for AI programming.
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Applications area of AI
• Perception
– Machine vision
– Speech understanding
– Touch ( tactile or haptic) sensation
• Robotics
• Natural Language Processing
– Natural Language Understanding
– Speech Understanding
– Language Generation
– Machine Translation
• Planning
• Expert Systems
• Machine Learning
• Theorem Proving
• Symbolic Mathematics
• Game Playing 24
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The History of AI
i. Gestation of AI (1943-1956)
ii. Early enthusiasm, great expectations (1952-1969)
iii. A dose of reality (1966-1974)
iv. Knowledge-based systems (1969-1979)
v. AI becomes an industry (1980-1988)
vi. Evolving systems (1986-present)
vii. Recent events (1987-present)
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Gestation of AI
• First work
– McCulloch and Pitts (1943): build a Boolean circuit model of brain
• First neural network computer
– Minsky and Edmonds (1951) build “SNARC” neural network computer
• First chess programs
– Shannon (1950) and Turing (1953)
• Official birthplace of AI
– The 1956 Dartmouth workshop organized by John McCarthy
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Early enthusiasm, great expectations
• Newell and Simon’s early work
– Logic Theorist(1956)
– General Problem Solver
• Samuel’s checkers program(1952-
1956)
• The MIT connection
– McCarthy’s LISP (1958)
– Minsky’s microworlds (1963)
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A dose of reality
• Things were not so easy…
– AI discovers computational complexity(1966-
1974)
– Only syntactic manipulation and little knowledge
– A lot of AI problems are intractable
– Basic structures had fundamental limitations
Can AI only be used for toy problems?
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Knowledge-based systems(1969-79)
• Expert systems
– Use knowledge to suit larger reasoning steps
– Inferring molecular structures: DENDRAL (1969)
– Medical diagnosis: MYCIN(1974)
– Geological system: PROSPECTOR
• Knowledge representation schemes
– Production System (Newell)
– Frame theory (Minsky)
– Conceptual Dependency (Schank)
– PROLOG (Colmerauer)
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AI becomes an industry
• The first commercial expert system
– DEC’s R1 (1982)
• The “Fifth Generation” project
– A Japanese initiative to take the lead in AI research
– Parallel reasoning machine
– Running Prolog like machine code
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Evolving systems(1990)
• Machine learning
– An attempt to solve the knowledge acquisition bottleneck
– Reinvention of the back propagation learning algorithm
• Expert system design is very time-consuming
– Can machine learning fix that problem?
• Intelligent tutoring
• Case based reasoning
• Multi agent planning
• Scheduling
• Natural language
• Virtual reality, games
• Genetic algorithms
– Evolutionary programming
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Recent events
• Based on existing theories instead of proposing new
ones
– Speech recognition based on hidden Markov models
– Planning based from the start on a simple framework
– Belief networks for reasoning about the combination of
uncertain evidence
• This has lead to robust methods and workable
research agendas
• Is the “whole agent” problem within reach?
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The Turing Test
Turing proposed operational test for intelligent
behavior in 1950.
Human
Human ?
Interrogator
AI system
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Alan M. Turing, (1912 - 1954)
• Major contributor to the code-breaking
work at Bletchley Park (Enigma), during
World War II.
• Major contributor to the early
development of computers.
• Foresaw Artificial Intelligence & devised
the Turing Test.
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Turing test
• To conduct Turing test, we need two people and the machine
to be evaluated. One person plays the role of the interrogator,
who is in a separate room from the computer and the other
person.
• The interrogator can ask questions of either the person or the
computer by typing questions and receiving typed responses.
however, the integrator knows only as A and B and aims to
determine which is the person and which is the machine.
• The goal of the machine is to fool the interrogator into
believing that it is the person. If the machine succeeds at this,
then we will conclude that the machine can think. The
machine is allowed to do whatever it can to fool the
interrogator.
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Information, Knowledge, Intelligence
• Information is a message that contains relevant meaning,
implication, or input for decision and/or action. Information
comes from both current (communication) and historical
(processed data or ‘reconstructed picture’) sources. In
essence, the purpose of information is to aid in making
decisions and/or solving problems or realizing an opportunity.
• Knowledge is the cognition or recognition (know-what),
capacity to act (know-how), and understanding (know-why)
that resides or is contained within the mind or in the brain.
The purpose of knowledge is to better our lives.
• Intelligence (also called intellect) is an umbrella term used to
describe a property of the mind that encompasses many
related abilities, such as the capacities to reason, to plan, to
solve problems, to think abstractly, to comprehend ideas, to
use language, and to learn. 36
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Human intelligence Vs. Artificial intelligence
Human intelligence Artificial intelligence
Human intelligence revolves around
adapting to the environment using a
combination of several cognitive
processes.
The field of Artificial intelligence focuses
on designing machines that can mimic
human behavior.
Human Intelligence is organically based, Artificial Intelligence is silicon based.
Human intelligence is separated through
the five senses.
Computer intelligence is separated into
binary code
Human memory...Five access points Artificial intelligence...Just one access
point....
The human mind on the other hand works
by asociation, which is not always logical
in a technical sense.
Artificially Intelligence works along pre-
set formulas and ways, it is rather straight
forward.
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Intelligent Computing Vs. conventional computing
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The AI Problem
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The AI Problem
• Much of the early work in the field focused on
formal tasks, such as game playing and theorem
proving.
• Another early foray into AI focused on the sort of
problem solving that we do every day called as
mundane tasks, such as commonsense
reasoning.
• The problem area where AI is now flourishing
most as a practical discipline are primarily the
domains that require only specialized expertise
without the assistance of commonsense
knowledge, called as expert systems.
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Formal Tasks
• Games
– Chess
– Backgammon
– Checkers-Go
• Mathematics
– Geometry
– Logic
– Internal calculus
– Proving properties of programs
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Mundane Tasks
• Perception
– Vision
– Speech
• Natural language
– Understanding
– Generation
– Translation
• Commonsense reasoning
• Robot control
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Expert Tasks
• Engineering
– Design
– Fault finding
– Manufacturing planning
• Scientific analysis
• Medical diagnosis
• Financial analysis
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Limits of AI Today
Today’s AI systems have been able to achieve limited success in some
of these tasks.
• In Computer vision, the systems are capable of face recognition
• In Robotics, we have been able to make vehicles that are mostly
autonomous.
• In Natural language processing, we have systems that are capable of
simple machine translation.
• Today’s Expert systems can carry out medical diagnosis in a narrow
domain
• Speech understanding systems are capable of recognizing several
thousand words continuous speech
• Planning and scheduling systems had been employed in scheduling
experiments with the Hubble Telescope.
• The Learning systems are capable of doing text categorization into
about a 1000 topics
• In Games, AI systems can play at the Grand Master level in chess
(world champion), checkers, etc.
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What can AI systems NOT do yet?
• Understand natural language robustly (e.g.,
read and understand articles in a newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Construct plans in dynamic real-time domains
• Exhibit true autonomy and intelligence
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What is an AI technique
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What is an AI technique
AI technique is a method that exploits knowledge which should be
represented in such a way that –
i. The knowledge captures generalization or we can say that
situations that share important properties and grouped together
rather than to represent separately each individual situation.
ii. It can be understood by people who provide it. In many AI
domains most of the knowledge, a programs has, must ultimately
be provided by people in terms they understand.
iii. It can easily be modified to correct errors and to reflect changes in
the world and in our world view.
iv. It can be used in a great many situations even if it is not totally
accurate or complete.
v. It can be used to help overcome its own sheer bulk by to narrow
the range of possibilities that must usually be considered.
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Examples of AI problems
1. Tic-Tac toe
2. Water jug problem
3. 8-puzzle problem
4. 8-queen problem
5. Chess problem
6. Missionaries and cannibals problem
7. Tower of Hanoi problem
8. Traveling salesman problem
9. Magic square
10. Language understanding problems
11. Monkey and Banana Problem
12. Crypt arithmatic puzzle
13. Block World problem
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Problem solving /Problem
representation
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Problem solving
• Problem solving is a process of generating solutions from
observed data.
• Key element of problem solving
– State: A state is a representation of problem at a given
moment.
– State space: Contains all the possible states for a given
problem.
– Operators: the available actions performed is called
operators.
– Initial state: position from which the problem-solving
process may start.
– Goal state: solution to the problem.
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General Problem solving
• To build a system, to solve a praticular problem, there are
four things:
1. Define the problem precisely (apply the State Space
representation).
2. Analyze the problem.
3. Isolate and represent the task knowledge that is
necessary to solve the problem.
4. Choose the best problem solving technique(s) and
apply it to the particular problem.
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Problem Characteristics
To choose an appropriate method for a particular
problem:
1. Is the problem decomposable?
2. Can solution steps be ignored or undone?
3. Is the universe predictable?
4. Is a good solution absolute or relative?
5. Is the solution a state or a path?
6. What is the role of knowledge?
7. Does the task require human-interaction?
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• Can the problem be broken down to smaller problems to be
solved independently?
• Decomposable problem can be solved easily.
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(x2 + 3x + sin2x.cos2x)dx
x2dx 3xdx sin2x.cos2xdx
(1  cos2x)cos2xdx
cos2xdx cos4xdx
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2. Can solution steps be ignored or
undone?
Theorem Proving
A lemma that has been proved can be ignored for next steps.
Ignorable!
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undone?
The 8-Puzzle
Moves can be undone and backtracked.
Recoverable!
2 8 3
1 6 4
7 5
1 2 3
8 4
7 6 5
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undone?
Playing Chess
Moves cannot be retracted.
Irrecoverable!
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undone?
• Ignorable problems can be solved using a simple
control structure that never backtracks.
• Recoverable problems can be solved using backtracking.
• Irrecoverable problems can be solved by recoverable style
methods via planning.
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The 8-Puzzle
Every time we make a move, we know exactly what will
happen.
Certain outcome!
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Playing Bridge
We cannot know exactly where all the cards are or what
the other players will do on their turns.
Uncertain outcome!
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• For certain-outcome problems, planning can used to generate a
sequence of operators that is guaranteed to lead to a solution.
• For uncertain-outcome problems, a sequence of generated
operators can only have a good probability of leading to a
solution.
Plan revision is made as the plan is carried out and the
necessary feedback is provided.
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4. Is a good solution absolute or relative?
1. Marcus was a man.
2. Marcus was a Pompeian.
3. Marcus was born in 40 A.D.
4. All men are mortal.
5. All Pompeians died when the volcano
erupted in 79 A.D.
6. No mortal lives longer than 150 years.
7. It is now 2004 A.D.
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4. Is a good solution absolute or
relative?
1. Marcus was a man.
erupted in 79 A.D.
Is Marcus alive?
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relative?1. Marcus was a man.
erupted in 79 A.D.
Is Marcus alive?
Different reasoning paths lead to the answer. It does not
matter which path we follow.
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relative?
The Travelling Salesman Problem
We have to try all paths to find the shortest one.
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relative?
• Any-path problems can be solved using heuristics that suggest
good paths to explore.
• For best-path problems, much more exhaustive search will be
performed.
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Finding a consistent intepretation
“The bank president ate a dish of pasta salad with
the fork”.
– “bank” refers to a financial situation or to a side of a river?
– “dish” or “pasta salad” was eaten?
– Does “pasta salad” contain pasta, as “dog food” does not
contain “dog”?
– Which part of the sentence does “with the fork” modify?
What if “with vegetables” is there?
No record of the processing is necessary.
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The Water Jug Problem
The path that leads to the goal must be reported.
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• A path-solution problem can be reformulated as a state-
solution problem by describing a state as a partial path to a
solution.
• The question is whether that is natural or not.
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6. What is the role of knowledge
Playing Chess
Knowledge is important only to constrain the search for a
solution.
Reading Newspaper
Knowledge is required even to be able to recognize a solution.
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7. Does the task require human-
interaction?
• Solitary problem, in which there is no intermediate
communication and no demand for an explanation of the
reasoning process.
• Conversational problem, in which intermediate
communication is to provide either additional assistance to the
computer or additional information to the user.
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State space representation
• Before a solution can be found, the prime condition is that the
problem must be very precisely defined. By defining it
properly, one converts the abstract problem into real workable
states that are really understood.
• A set of all possible states for a given problem is known as the
state space of the problem.State space representations are
highly beneficial in AI because they provide all possible states,
operations and goals.
• If the entire state space representations for a problem is given,
it is possible to trace the path from the initial state to the goal
state and identify the sequence of operators necessary for
doing it.
• The major deficiency of this method is that it is not possible to
visualize all states for a given problem. Moreover, the
resources of the computer system are limited to handle huge
state-space representation. 72
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State space representation of coffee
making
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8- puzzle problem
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• In the 8-puzzle problem we have a 3×3 square
board and 8 numbered tiles. The board has one
blank position.
• Tiles can be slid to adjacent blank positions. We
can alternatively and equivalently look upon this
as the movement of the blank position up, down,
left or right.
• The objective of this puzzle is to move the tiles
starting from an initial position and arrive at a
given goal configuration.
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Initial and goal state
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The start state is some
(almost) random
configuration of the tiles
The goal state is as shown
Operators are
Move empty space up
Move empty space down
Move empty space right
Move empty space left
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State space of the 8 puzzle problem
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8- puzzle problem
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Solution
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A Water Jug Problem
• You have a 4-
gallon and a 3-
gallon water jug
• You have a pump
with an unlimited
amount of water
• You need to get
exactly 2 gallons
in 4-gallon jug 79
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Puzzle-solving as Search
• State representation: (x, y)
– x: Contents of four gallon
– y: Contents of three gallon
• Start state: (0, 0)
• Goal state (2, n)
• Operators
– Fill 3-gallon from pump, fill 4-gallon from pump
– Fill 3-gallon from 4-gallon , fill 4-gallon from 3-gallon
– Empty 3-gallon into 4-gallon, empty 4-gallon into 3-
gallon
– Dump 3-gallon down drain, dump 4-gallon down drain
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State Space Search: Water Jug
Problem...
1. (x, y)  (4, y) if x is less than 4, fill the 4 gallon jug
if x  4
2. (x, y)  (x, 3) fill the 3-gallon jug
if y  3
3. (x, y)  (x  d, y) pour some water out of the 4 gallon jug
if x  0
4. (x, y)  (x, y  d) pour some water out of the 3 gallon jug
if y  0
5. (x, y)  (0, y) empty the 4-gallon jug on the ground
if x  0
6. (x, y)  (x, 0) empty the 3-gallon jug on the ground
if y  0
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Water Jug Problem...
7. (x, y)  (4, y  (4  x))
if x  y  4, y  0
8. (x, y)  (x  (3  y), 3)
if x  y  3, x  0
9. (x, y)  (x  y, 0)
if x  y  4, y  0
10. (x, y)  (0, x  y)
if x  y  3, x  0
11. (0, 2)  (2, 0)
12. (2, y)  (0, y)
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pour water from the 3- gallon jug into the 4-
gallon jug until the 4-gallon jug is full
pour water from the 4- gallon jug into the 3-
gallon jug until the 3-gallon jug is full
pour all the water from the 3-gallon jug into
the 4-gallon jug
pour all the water from the 4-gallon jug into
the 3-gallon jug
pour the 2 gallons from the 3-gallon jug into
the 4-gallon jug
empty the 2 gallons in the 4 gallon jug on the
ground
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State Space Search: Water Jug
Problem...1. Current state = (0, 0)
2. Loop until reaching the goal state (2, 0)
- Apply a rule whose left side matches the current state
- Set the new current state to be the resulting state
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Gallons in the 4-
Gallon Jug
Gallons in the 3-
Gallon Jug
Rule Applied
0 0 2
0 3 9
3 0 2
3 3 7
4 2 5
0 2 9
2 0
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Missionaries and cannibals
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The missionaries and
cannibals wish to cross a
river
They have a boat that can
hold two people
It is unsafe to have cannibals
outnumber missionaries
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Missionaries and cannibals
M
M
M
C
C C
Initial state
M
M
M
C
C C
Goal state
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States
• A state can be represented by the number of missionaries
and cannibals on each side of the river
• State(#m, #c, riverbank)
#m  no. of missionaries
#c  no. of cannibals
Bankofriver indicates whether the boat in the left bank or
right bank 0 left bank, 1right bank
• Initial state (3,3,0)
• Goal state: (0,0,0)
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Operations
• An operation takes us from one state to another
• Here are five possible operations:
– boat takes 1 missionary across river (1m)
– boat takes 1 cannibal across river (1c)
– boat takes 2 missionaries across river (2m)
– boat takes 2 cannibals across river (2c)
– boat takes 1 missionary and 1 cannibal across river (1m1c)
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The state space
3m, 2c
3m, 1c
2m, 3c
1m, 3c
2m, 2c
1m
2m
2c
1m1c
1c
1c
3m, 2c,
boat
2m, 3c,
boat
1m
1m1c
3m, 3c,
boat
etc.
etc.
etc.
etc.
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Solution
1. (3, 3,0)  (0,0,1) Initial state
2. (3, 1,0)  (0,2,1) two cannibal will go
3. (3,2,0)  (0,1,1) one cannibal will return
4. (3,0,0) (0,3,1) two cannibal will go
5. (3,1,0) (0,2,1) one cannibal will return
6. (1,1,0) (2,2,1) two Missionaries will go
7. (2,2,0) (1,1,1) one cannibal and one Missionaries
will return
8. (0,2,0) (3,1,1) two Missionaries will go
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Farmer river crossing Problem
• A farmer with his wolf, goat and cabbage
come to the edge of a river they all want to
cross the river. There is a boat at the rivers
edge, but only the farmer can row, the boat
can carry two things at a time. if the wolf is
ever left alone with the goat, the wolf will eat
the goat, similar, if the goat is left alone with
the cabbage, the goat will eat the cabbage.
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Solution
• A set of all possible states for a problem is
known as the state space of the problem.
There are 16 sub-states of the man , wolf, goat
and cabbage.
• Some of the 16 states such as GC-MW are
fatal and may never be entered by the system.
MWGC-φ  initial state
φ –MWGC  goal state
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Sequence of steps for crossing river
1. First man crosses the river with goat.
2. Man comes back.
3. Man takes either cabbage (or wolf) with him
to another side.
4. Man comes back with goat to first side.
5. Man takes wolf (or cabbage) with him to
another side.
6. Man comes back.
7. Man takes goat with him to another side.
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State space representation
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94
n-queens
• States: 4 queens in 4 columns (44 = 256 states)
• Actions: move queen in column
• Goal test: no attacks
• Evaluation: h(n) = number of attacks
• Given random initial state, we can solve n-queens for large n
with high probability
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Search and Control strategies
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Production system
Since search forms the core of many intelligent processes, it is
useful to structure AI programs in a way that facilitates
describing and performing the search process. Production
systems provide such structures.
A production system consist of:-
 A set of rules, each consisting of a left side (pattern) that
determines the applicability of the rule and a right side
describing the operation to be performed.
 One or more knowledge/databases that contain whatever
information is appropriate for the particular task.
 A control strategy that specifies the order in which the
rules will be compared to the database and a way of
resolving the conflicts that arise when several rules match
at once.
 A rule applier which is the computational system that
implements the control strategy and applies the rules.
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Classes of Production System
• Monotonic production system: the application of a rule never
prevents the later application of another rule that could also
have been applied at the time the first rule was selected.
• Non-monotonic production system: Is one in which this is
not true.
• Partially commutative production system: the application of
a particular sequence of rules transforms state x into state y,
then any permutation of those rules that is allowable also
transforms state x into state y.
• Commutative production system: system that is both
monotonic and partially commutative.
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Control strategies
• A control strategy that specifies the order in which the rules will
be applied.
• Control strategies help us to overcome the abnormal situations,
when there are more than one rules or fewer than one rule will
have its left sides match the current state.
• Requirement for control strategy
i. A good control strategy causes motion
ii. A good control strategy is systematic
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Example
99
(0, 0)
(4, 0) (0, 3)
(1, 3)(0, 0)(4, 3) (3, 0)(0, 0)(4, 3)
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Search Strategies
100
1. Uninformed search (blind search)(Exhaustive search)(Brute
force)
Having no information about the number of steps from the
current state to the goal.
2. Informed search (heuristic search)
More efficient than uninformed search.
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Brute Force or Uninformed Search
Strategies
101
 These are commonly used search procedure which
explore all the alternatives during the search process.
 They do not have any domain specific knowledge.
 They need the initial state, the goal state and a set of
legal operators.
 The strategy gives the order in which the search space is
searched
 The followings are example of uninformed search
– Depth First Search (DFS)
– Breadth First Search (BFS)
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Search Strategies: Blind Search
102
• Breadth-first search
Expand all the nodes of
one level first.
• Depth-first search
Expand one of the nodes at
the deepest level.
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Depth First Search
• The search begins by expanding the initial node, generate
all successors of the initial node and test them.
• Depth-first search always expands the deepest node in the
current frontier of the search tree.
• Depth-first search uses a LIFO approach.
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104Prepared by : -Agniwesh Mishra, RCET,
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Depth-first search
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113
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Algorithm for Depth First Search
1. If the initial state is a goal state, quit and return success.
2. Otherwise, do the following until success or failure is
signaled:
a) Generate a successor, E, of the initial state. If there are
no more successors, signal failure.
b) Call Depth-First Search with E as the initial state.
c) If success is returned, signal success. Otherwise
continue in this loop.
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Time and space complexity
Time Complexity :
1 + b + b2 + b3 +…+……bd.
Hence Time complexity = O (bd)
Where b-> branching factor
d-> Depth of a tree
Space Complexity :
Hence Space complexity = O (d)
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Advantages of Depth-First Search
i. It requires less memory since only the nodes
of the current path are stored.
ii. By chance, it may find a solution without
examining much of the search space at all.
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Disadvantages of Depth-First Search
i. Determination of the depth until which the
search has to proceed. This depth is called
cut-off depth.
ii. If the cut-off depth is smaller, solution may
not be found.
iii. If cut-off depth is large, time complexity will
be more.
iv. And there is no guarantee to find a minimal
solution, if more than one solution exists.
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Breadth First Search
• Searching processes level by level unlike depth first
search which goes deep into the tree.
• An operator is employed to generate all possible children
of a node.
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Breadth-first search
•Guaranteed to find shortest solution first
•best-first finds solution a-c-f
•depth-first finds a-b-e-j
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Algorithm of Breadth First Search
1. Create a variable called Node-LIST and set it to the initial
state.
2. Until a goal state is found or Node-LIST is empty:
a) Remove the first element from Node-LIST and call it E. if
Node-LIST was empty, quit.
b) For each way that each rule can match the state described
in E do:
i. Apply the rule to generate a new state,
ii. If the new state is a goal state, quit and return this
state.
iii. Otherwise, add the new state to the end of Node-LIST.
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Time and space complexity
Time Complexity :
1 + b + b2 + b3 +…+……bd.
Hence Time complexity = O (bd)
Space Complexity :
1 + b + b2 + b3 +…+……bd.
Hence Space complexity = O (bd)
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Advantages of Breadth-First Search
i. Breadth first search will never get trapped
exploring the useless path forever.
ii. If there is a solution, BFS will definitely find it
out.
iii. If there is more than one solution then BFS
can find the minimal one that requires less
number of steps.
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Disadvantages of Breadth-First Search
i. It requires more memory
ii. Searching process remembers all unwanted
nodes which is of no practical use for the
search.
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DFS Vs BFS
DFS BFS
It require less memory because only
the nodes on the current path are
stored.
It require more memory because all the
tree that has so far been generated
must be stored.
It is one in which by luck solution can
be found without examining much of
the search space at all.
While in BFS all parts of the tree must
be examined to level n before any
nodes on level n+1 can be examined.
It does not give optimal solution. It gives optimal solution.
DFS may find a long path to a solution
in one part of the tree, when a shorter
path exists in some other, unexplored
part of the tree.
BFS guarantees to find a solution if it
exists. Furthermore if there are
multiple solutions, then a minimal
solution will be found.
Time complexity: O(bd )
where b : branching factor, d: depth
Time complexity: O(bd )
Space complexity: O(d) , d: depth Space complexity: O(bd )
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Informed Search
• Informed search tries to reduce the amount of
search that must be done by making
intelligent choices for the nodes that are
selected for expansion.
• In general this is done using
a heuristic function.
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Heuristic Function
• A heuristic function is a function that ranks alternatives in
various search algorithms at each branching step based on the
available information (heuristically) in order to make a
decision about which branch to follow during a search.
• Well designed heuristic functions can play an important part in
efficiently guiding a search process toward a solution.
Sometimes very simple heuristic functions can provide a fairly
good estimate of whether a path is any good or not. In other
situations, more complex heuristic functions should be
employed.
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Heuristic Example : 8-puzzle
134
The first picture shows the current state and the second picture
the goal state.
Heuristics  is the number of tiles out of place.
h(n) = 5
because the tiles 2, 8, 1, 6 and 7 are out of place.
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Heuristic Search Algorithm
• Algorithms that use a heuristic function are as follows
– Hill Climbing
– Best First Search
– A*
– AO*
– Constraint satisfaction
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Hill Climbing
• This algorithm also called discrete optimization algorithm.
• It utilizes a simple heurisitic function.
• Hill Climbing = Depth First Search + Heuristic Function
• There is practically no difference between hill climbing
and depth first search except that the childern of the node
that has been exapanded are sorted by the remaining
distance.
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Implementation of Hill Climbing
• There are two ways to implement hill climbing
– Simple hill climbing
– Steepest-Ascent hill climbing or gradient search
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Simple hill climbing algorithm
1. Evaluate the initial state if goal then return (success) . Else continue
with initial state as the current state .
2. Loop until a solution is found or until there are no new operator to
apply to current node :
a) Select a new operator and apply current state to produce a
new state .
b) Evaluate the new state.
i. if it is a goal then return (success) .
ii. if not goal but better than current state then make it the
current state .
iii. if it is not better than current state then continue the
loop.
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Steepest-Ascent hill climbing
1. Evaluate the initial state . If it is also a goal state , then return it
and quit . Otherwise , continue with the initial state as current
state .
2. Loop until a solution is found or until a complete iteration
produces no change to current state :
a) Let SUCC be a state such that any possible successor of
the current state will be better than SUCC .
b) For each operator that applies to the current state do :
i) Apply the operator and generate a new state .
ii) Evaluate the new state . if it is a goal state , return it and
quit . If not, compare it to SUCC . If it is better , then set
SUCC to this state . if it is not better , then leave SUCC
alone .
iii) if the SUCC is better than current state , then set
current state to SUCC . 139
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Difference between simple &
steepest-ascent hill climbing
• Steepest-ascent hill climbing or gradient search considers all
the moves from the current state and selects the best one as the
next state.
• In the simple hill climbing the first state that is better than the
current state is selected.
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Search Tree for Hill Climbing
Goal Node
Root
A
B C
FED
8
3
7
2.7 2
2.9
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Example
142
Goal Node
1
4
2
2 3
1
A
B C D
(1) (2) (4)
E F
(2) (3)
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Problems with Hill Climbing
Technique
• Local Maximum : A state that is better than all its neighbours but
not so when compared to states to states that are farther away.
Global PeakLocal Maximum
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144
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Problem with Hill Climbing Technique
Plateau :A flat area of the search space in which all neighbouring
states have the same value.
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146
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Problem with Hill Climbing Technique
Ridge :The orientation of the high region, compared to the set
of available moves, makes it impossible to climb up. However,
two moves executed serially may increase the height.
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Methods to overcome these problems
• Backtracking for local maximum. Backtracking
helps in undoing what has been done so far and
permits to try totally different path to attain the
global peak.
• A big jump is the solution to escape from the
plateau. A huge jump is recommended because in
a plateau all neighboring points have the same
value.
• Trying different paths at the same time is the
solution for circumventing ridges.
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Best First Search
• It is a way of combining the advantages of both depth-first
search and breadth first search into a single method.
• One way of combining the DFS and BFS is to follow a
single path at a time, but switch paths whenever some
competing path looks more promising than the current one
does.
• At each step of the best-first search process, we select the
most promising of the nodes we have generated so far.
This is done by applying an appropriate heuristic function
to each of them. We then expand the chosen node by using
the rules to generate its successors. If one of them is a
solution, we can quit. If not, all those new nodes are
added to the set of nodes generated so far. Again the most
promising node is selected and the process continues.
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Best First Search Example
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List to maintain in Best-First Search
• OPEN: nodes that have been generated, but have not
examined. This is organized as a priority queue.
• CLOSED: nodes that have already been examined. Whenever
a new node is generated, check whether it has been generated
before.
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Algorithm of Best First Search
1. OPEN = {initial state}.
2. Loop until a goal is found or there are no nodes left in
OPEN do:
a. Pick the best node in OPEN
b. Generate its successors.
c. For each successor do:
i. If it has not been generated before, evaluate it,
add it to OPEN, and record its parent.
ii. If it has been generated before, change the
parent if this new path is better than the
previous one. In that case, update the cost of
getting to this node and to any successors that
this node may already, have.
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A Sample tree for best first search
Start Node
M
I L
K
J
B
A
C
E
D
F
G
H
3
6
5
9
8
12
14
7
5
6
1
0
2
Goal Node
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Search process of best first search
Step Node
being
expanded
Children OPEN List CLOSE List
1 S (A:3)(B:6)(C:5) (A:3)(B:6)(C:5) (A;3)
2 A (D:9)(E:8) (B:6)(C:5) (D:9)(E:8) (C:5)
3 C (H:7) (B:6) (D:9) (E:8) (H:7) (B:6)
4 B (F:12) (G:14) (D:9) (E:8) (H:7) (F:12) (G:14) (H:7)
5 H (I:5) (J:6) (D:9) (E:8) (F:12) (G:14)
(I;5) (J:6)
(I:5)
6 I (K:1) (L:0)
(M:2)
(D:9) (E:8) (F:12) (G:14)
(J:6) (K:1)(L:0)(M:2)
Search stops as
goal is reached
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A*
• A* algorithm was given by Hart, Nilsson and Rafael in
1968.
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157
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Example
Obtain the fitness number for node K
F(n)=g(n)+h(n)
=(cost function involved from start node S to node K)+(evaluation
function for K)
=6+5+7+1+1
=20
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A* Algorithm
1. Initialize: set OPEN= {s}, CLOSED={ }
g(s)=0, f(s)=h(s)
2. Fail: if OPEN ={ }, Terminate & fail.
3. Select: select the minimum cost state, n, from OPEN. Save n in
CLOSED.
4. Terminate: if n  G, terminate with success, and return f(n).
5. Expand: for each successor , m, of n
If m ∉[open U closed]
Set g(m) =g(n) + C(n,m)
Set f(m) =g(m) + h(m)
Insert m in OPEN.
If m [open U closed]
Set g(m) =min{g(m) ,g(n)+ C(n,m)}
Set f(m) =g(m) + h(m)
If f(m) has decreased and m CLOSED, move m to OPEN
6. Loop: go to step 2 159
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Example
160
OPEN
1(5)
2(7) 3(25)
3(25) 4(9)
3(25) 5 (11)
3(25) 6(28)
4(7) 6(28)
6(28) 5(9)
6(28) 6(26)
CLOSED
1 (5) 2 (7) 4 (9)
5 (11) 3 (25) 4 (7) 5 (9)
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Merit and demerit of A* Algorithm
Merits
A* is both complete and admissible. Thus A* always finds an
optimal path, if one exists.
Demerits
It is costly if the computation cost is high.
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Problem Reduction
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Problem Reduction
• Sometimes problems only seem hard to solve. A hard problem
may be one that can be reduced to a number of simple
problems...and, when each of the simple problems is solved,
then the hard problem has been solved.
• Problem reduction may be defined as planning how best to
solve a problem that can be recursively decomposed into
subproblems in multiple ways.
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AND or OR
• The complex problem and the sub problem , there exist
two kinds of relationships.
– AND relationship
– OR relationship.
• In AND relationship, the solution for the problem is
obtained by solving all the sub problems.
• In OR relationship, the solution for the problem is
obtained by solving any of the sub problem.
• An arc ( ) connecting different branches is called
AND.
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AND/OR graphs
• Real life situations do not exactly decompose into either AND
tree or OR tree but are always a combination of both.
• AND/OR graph is useful for representating the solutions of
problem that can be solve by decomposing them into a set of
smaller problems.
• A* algorithm is not adequate for AND/OR graphs.
• AO* algorithm is used for AND/OR graphs.
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AND/OR tree
A
B C
D
547 6
9
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167
Example
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AO* Algorithm
1. Initialize: set G * = {s}, f(s)=h(s)
if s  T, label s as SOLVED
2. Terminate: if s is SOLVED, then terminate.
3. Select: select a non-terminal leaf node n from the marked sub
tree.
4. Expand: make explicit the successors of n
For each new successor, m:
set f(m)=h(m)
if m is terminal, label m SOLVED
5. Cost revision: call cost-revise(n)
6. Loop: Go to step 2
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Cost-revise(n)
1. create Z={n}
2. If Z={} return
3. Select a node m from z such that m has no descendants in Z.
4. If m is an AND node with successors.
r1 r2 r3 ………………….. rk
Set f(m) = [f(ri) + C(m, ri )]
Mark the edge to each successor of m.
If each successor is labeled SOLVED, then label m as SOLVED.
5. If m is an OR node with successors.
r1 r2 r3 ………………….. rk
Set f(m) = min[f(ri) + C(m, ri )]
Mark the edge to best successor of m.
If the marked successor is labeled SOLVED, then label m as SOLVED.
6. If the cost or label of m has changed, then insert those parents of m into z
for which m is a marked successor.
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Example
170
Illustrate the operation of AO* search upon the following
search space.
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Constraint Satisfaction
 Many AI problems can be viewed as problems of constraint
satisfaction.
Examples
– Scheduling
– Timetabling
– Supply Chain Management
– Graph colouring
– Puzzles
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Constraint Satisfaction Problem(CSP)
• A CSP consists of
– A set of variables, X
– For each variable x i in X, a domain Di
– Di is a finite set of possible values
• A solution is an assignment of a value in Di to each
variable x i such that every constraint satisfied.
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Crypt-arithmetic puzzle
• We have every letters standing for a digit and
every letter stands for a different digit.
• We have to find an assignment of letters to digits
such that a given arithmetic formula is correct.
• Variables are D, E, M, N, O, R, S, Y
• Domains are
– {0, 1,2,3,4,5,6,7,8,9} for D, E,N,O,R,Y
– {1,2,3,4,5,6,7,8,9} for S, M

173
SEND
MORE
MONEY
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Constraints for this problem
Constraint 1:
We can write one long constraint for the sum.
1000* S + 100* E+10* N+ D
+ 1000* M+ 100* O+10* R+ E
………………………………………….
10000*M + 1000* O+ 100*N+ 10* E+ Y
Constraint 2:
alldifferentr(D, E, M, N, O, R, S, Y)
These two constraints express the problem precisely.
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Solution
• Rules for propagating constraints generates the following
constraints:
1. M = 1, since two single-digit numbers plus a carry can not total
more than 19.
2. S = 8 or 9, since S+M+C3 > 9 (to generate the carry) and M = 1,
S+1+C3>9, so S+C3 >8 and C3 is at most 1.
3. O = 0, since S + M(1) + C3(<=1) must be at least 10 to generate a
carry and it can be most 11. But M is already 1, so O must be 0.
4. N = E or N=E+1, depending on the value of C2. But N cannot have
the same value as E. So N = E+1 and C2 is 1.
5. In order for C2 to be 1, the sum of N + R + C1 must be greater than
9, so N + R must be greater than 8.
6. N + R cannot be greater than 18, even with a carry in so E cannot be
9.
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Solution...
• Suppose E is assigned the value 2.
• The constraint propagator now observes that:
• N = 3 since N = E + 1.
• R = 8 or 9, since R + N (3) + C1 (1 or 0) = 2 or 12. But
since N is already 3, the sum of these nonnegative
numbers cannot be less than 3. Thus R + 3 +(0 or 1) = 12
and R = 8 or 9.
• 2 + D = Y or 2 + D = 10 + Y, fro the sum in rithmost
column.
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M = 1
S = 8 or 9
O = 0
N = E + 1
C2 = 1
N + R > 8
E  9
N = 3
R = 8 or 9
2 + D = 10* C1 + Y
C1+N+R=10+E
2 + D = Y
N + R = 10 + E
R = 9
S =8
2 + D = 10 + Y
D = 8 + Y
R=8, S=9
Y = 0 Y = 1
Start
E = 2
C1 = 0 C1 = 1
D = 8 D = 9
Initial state:
• No two letters have
the same value.
• The sum of the digits
must be as shown.
SEND
MORE
MONEY

M=1
R = 9
S=8
E=2
N=3
O=0
D = 4
Y =6
M=1
R = 8
S=9
E=2
N=3
O=0
D=8
Y=0
M=1
R = 8
S=9
E=2
N=3
O=0
D=9
Y=1
Conflict Conflict
Conflict
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M = 1
S = 8 or 9
O = 0
N = E + 1
C2 = 1
N + R > 8
E  9
N = 6
R = 8 or 9
5 + D = 10* C1 + Y
C1+N+R=10+E
5 + D = Y
N + R = 10 + E
R = 9
S =8
R=8, S=9
5 + D = 10 + Y
D = 5 + Y
D = 7
Y=2
Start
E = 5
C1 = 0 C1 = 1
Initial state:
• No two letters have
the same value.
• The sum of the digits
must be as shown.
SEND
MORE
MONEY

M=1
R = 9
S=8
E=5
N=6
O=0
D = 2
Y =7
M=1
R = 8
S=9
E=5
N=6
O=0
D=7
Y=2
Conflict
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Final solution
• M=1
• R = 8
• S=9
• E=5
• N=6
• O=0
• D=7
• Y=2
• C1=1
• C2=1
• C3=0
• C4=1
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SOLVE
• US +AS=ALL
• SHE +THE=BEST
• CROSS+ROADS=DANGER
• DAYS+TOO=SHORT
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Solve
CROSS
+ROADS
…………………
DANGER
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Solution
Rule 1: Well you can see that the DANGER has one more letter than CROSS
and ROADS, and the extra letter is D. That means that C + R equals
something more than 10. Which also means D is 1.
Rule 2: Oh look, S + S = R. That means that R must be even. We have a choice
of 4, 6 and 8, because if R was 2, S would have to be 1, and D is already 1.
Let's try 6 for the value of R, because we need high numbers if we want C
+ R to equal something more than 10. Oh look, if R is 6 and S is R divided
by 2, then S must be 3!
Rule 3: S+D=E, 3+1=4, So, E=4
Rule 4: And since we now only have 4 spots in the key left, we choose the
highest number for C, which is 9. Again, we need high numbers to make C
+ R equal something more than 10.
Rule 5: In the equation, O + A = G. We have 2, 5, 7 and 8 vacant. Let's play
around with these letters. Let's see if we can find an equation in there. Yes!
There is an equation there. 5 + 2 = 7! So G must equal 7. We know that 9 +
6 = 15, but it's missing the 5! So, A must equal 5. In turn, this leads to O
having to be 2 (do the maths, O + 5 = 7). And last of all, O is 2, so 6 + 2 (6
+ O) = N But 6 + 2 = 8, so now N is 8. We now have the following 182
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• 96233
62513
------------
158746
And the following key...
• C=9
• R=6
• O=2
• S=3
• A=5
• D=1
• E= 4
• N=8
G=7
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SOLVE
•
184
DAYS
+ TOO
………
SHORT
9871
655
………….
10526
US
+AS
……
ALL
85
15
………
100
SHE
+THE
……..
BEST
634
834
……………..
1468
CROSS
+ROADS
……………
DANGER
96233
62513
…………..
158746
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Games in Artificial Intelligence
Why has game playing been a focus of AI?
• Games have well-defined rules, which can be implemented in
programs
• Interfaces required are usually simple
• Many human expert exist to assist in the developing of the
programs.
• Games provide a structured task wherein success or failure
can be measured with least effort.
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Game Playing (Basic strategy)
• John von Neumann is acknowledged as father of game
theory.
• The term Game means a sort of conflict in which n
individuals or groups (known as players) participate.
• Game theory denotes strategy for game.
• Grow a search tree
• Only one player move at each turn
• At the leaf position, when the game is finish, assign the utility
to player.
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Two-player games
Usual conditions:
• Each player has a global view of the board
• Zero-sum game: any gain for one player is a loss for the other
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Major components of a game playing
program
Two major components
• Plausible move generator: plausible move generator is used
to generate the set of possible successor positions.
• Static evaluation function generator (utility function):
based on heuristics, this generates the static evaluation
function value for each and every move that is being made.
The static evaluation function gives a snapshot of a particular
move.
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189
Game Tree
The computer is Max.
The opponent is Min.
At the leaf nodes, the
utility function
is employed. Big value
means good, small is bad.
computer’s
turn
opponent’s
turn
computer’s
turn
opponent’s
turn
leaf nodes
are evaluated
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Game playing strategies
• Minimax strategy
• Alpha-Beta Pruning
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Minimax Strategy
• It is a simple look ahead strategy for two person game playing.
• One player “maximizer” tries to maximize the utility function
• Other player “minimizer” tries to minimize the utility function
• The plausible move generator generates the necessary states
for further evaluation and the static evaluation function
“ranks” each of the positions.
• To decide one move, it explores the possibilities of winning by
looking ahead to more than one stop. This is called ply. To
decide the current move, game tree would be explored two
levels farther.
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MiniMax Example
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MiniMax Example
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MiniMax Example
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MiniMax Example
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MiniMax Example
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Minimax Algorithm Illustrated
2 7 1 8
MAX
MIN
2 7 1 8
2 1
2 7 1 8
2 1
2
2 7 1 8
2 1
2Move selected by minimax
Static evaluation
Value returned
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Minimax Algorithm
function MINIMAX(N) is
begin
if N is a leaf then
return the estimated score of this leaf
else
Let N1, N2, .., Nm be the successors of N;
if N is a Min node then
return min{MINIMAX(N1), .., MINIMAX(Nm)}
else
return max{MINIMAX(N1), .., MINIMAX(Nm)}
end MINIMAX;
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Example 1: Considering the following
game tree search space
199
•Which move should be chosen under min-max search procedure, if the
first move is a maximizing move?
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– If the first player is a maximizing player, what move
should be chosen under the min-max strategy?
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201
Find out optimal path and value of the following example
with the help of min-max algorithm
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Alpha-Beta Pruning
• The problem with Mini-Max algorithm is that the number
of game states it has to examine is exponential in the
number of moves.
• The Alpha-Beta Pruning helps to arrive at correct Min-Max
algorithm decision without looking at every node of the
game tree.
• Applying an alpha-cutoff means we stop search of a
particular branch because we see that we already have a
better opportunity elsewhere.
• Applying a beta cutoff means we stop search of a particular
branch because we see that the opponent already has a
better opportunity elsewhere.
• Applying both forms is alpha beta pruning.
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Alpha Beta Procedure
• Depth first search of game tree, keeping track of:
– Alpha: Highest value seen so far on maximizing level
– Beta: Lowest value seen so far on minimizing level
• Pruning
– When Maximizing,
• do not expand any more sibling nodes once a node has been
seen whose evaluation is smaller than Alpha
– When Minimizing,
• do not expand any sibling nodes once a node has been seen
whose evaluation is greater than Beta
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204
α-β pruning example
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205
alpha cutoff
 = 3
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207
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Alpha-beta algorithm
function MAX-VALUE (state, game, alpha, beta)
;; alpha = best MAX so far; beta = best MIN
if CUTOFF-TEST (state) then return EVAL (state)
for each s in SUCCESSORS (state) do
alpha := MAX (alpha, MIN-VALUE (state, game,
alpha, beta))
if alpha >= beta then return beta
end
return alpha
function MIN-VALUE (state, game, alpha, beta)
if CUTOFF-TEST (state) then return EVAL (state)
for each s in SUCCESSORS (state) do
beta := MIN (beta, MAX-VALUE (s, game, alpha, beta))
if beta <= alpha then return alpha
end
return beta
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210
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Example 1: Prune this tree
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212
•What node would not be needed to be examined using alpha-beta
pruning technique?
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– What nodes should not be needed to be examined
using alpha-beta pruning technique?
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Games of chance
• In real life, many unpredictable external events can put us into
unforeseen situations.
• Many games mirror this unpredictability by including a
random element, such as the throwing of STOCHASTIC
GAMES dice. We call these stochastic games or games of
chance.
• Backgammon is a typical game that combines luck and skill.
Dice are rolled at the beginning of a player's rum to determine
the legal moves in the backgammon position.
• In game tree in backgammon must include chance nodes in
addition to MAX and MIN nodes.
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Schematic game tree for a
backgammon position
The
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Games of chance
• The next step is to understand how to make correct decisions.
Obviously, we still want to pick the move that leads to the best
position. However, positions do not have definite minimax
values. instead, we can only calculate the expected value of a
position: the average over all possible outcomes of the chance
nodes.
• This leads us to generalize the minimax value for deterministic
games to an expecti_minimax value for games with chance
nodes. Terminal nodes and MAX and MIN nodes (for which
the dice roll is known) work exactly the same way as before.
For chance nodes we compute the expected value, which is the
sum of the value over all outcomes, weighted by the
probability of each chance action.
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Artificial Intelligence Notes Unit 1

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Artificial Intelligence Notes Unit 1