Agent and the Knowledge Representation-6_411

8/7/2019 Agent and the Knowledge Representation-6_411

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ability to reuse methods in different branches of

the hierarchy.

The authority for decision making and

control in concentrated in a single problem solver

at each level of hierarchy . Interaction is through

vertical communication from superior to sub-

ordinate agent and vice-versa . [3,4]

2.2 Modular Organization

A modular organization is one in which

different functional components are separated

from one another, a technique adopted from

software engineering. This is in contrast to a

composite organization in which there is no

separation between functions. Modular

organization is also distinct from hierarchical

organization. Modular organization is chiefly

concerned with the horizontal design of a system

whereas hierarchical organization involves a

consideration of the vertical nature of the design.

Thus, each level in a hierarchical system may be

sub-divided into functionally-distinct modules.

3 KNOWLEDGE REPRESENTATION

Knowledge means intellectual acquaintance with

perception of fact or truth. Representation is a

way of describing certain fragments or

information so that any reasoning system can

easily adopt it for inferencing purposes.

Knowledge Representation system should

provide way of representing complex knowledge

and should possess the following characteristics:

1. The representation scheme should have a set

of well defined syntax and semantics. Thiswill help in representing various kinds of

knowledge .

2. The knowledge representation scheme shouldhave a good expressive capacity . A good

expressive capacity will catalyze the

inferencing mechanism in its reasoning

process.The philosophy behind Subsumption Architecture

is that the world should be its own model.

According to Brooks, storing models of the world

is dangerous in dynamic, unpredictable

environments because representations might be

incorrect or outdated. What is needed is the abilityto react quickly to the present. Thus, sensor

readings should be mapped quickly and directly to

actuator commands in a decentralized fashion.

Agents designed according to Subsumption

Architecture are generally non-symbolic. They

have no global representation, and are

decentralized.

In ATALANTIS architecture the control

layer uses procedural knowledge while the

deliberative layer uses declarative knowledge.

THEO is frame based architecture .For an

architecture to be frame-based means that it is

organized around the representation of all of its

knowledge in frames. This is important because it

allows a uniform representation of knowledge. In

a frame based system, all knowledge is

represented as slots of some concept which

describe properties of that concept

All knowledge in PRODIGY including

control rules, domain knowledge, learned

knowledge is represented in the PRODIGY

description language (PDL). PDL is based on

first-order predicate calculus and includes

variables, disjunction and conjunction, universal

and existential quantification, and conditional

representation.

In Icarus, the Concept tree in Labyrinth stores

all knowledge. The other components of the

architecture have access to this knowledge

explicitly. For example, Daedalus retrieves

explicit plans from Labyrinth. The use ofdeclarative representations allows each module to

reason about the available knowledge . [8]

The symbolic world model or global database

in AIS is formed from a uniform and declarative

based conceptual network. Within this

representation, sub-networks can represent

actions, events, and control plans through

symbolic abstraction. The generality of the

conceptual network representation permits

encoding complete knowledge from different

problem domains, enhancing multi-faceted

expertise. The network-type representation

facilitates explanation as well .All knowledge in MAX agents exists in

nested logic frames including the rules and

operators. Separating rules and operators

integrates a domain theory, in which rules specify

actions and actions satisfy rules. With a

production memory architecture knowledge is

brought to bear on a problem by executing some

operator that uses that knowledge, i.e. it adds

symbols and it deletes symbols in some logical or

rational fashion

Homer uses both Black-box representation

and heterogeneous representation .SOAR uses

Black-box representation .Teton , RALPH-MEAand Entropy Reduction Engine architectures use

both Declarative and Procedural representation .

3.1 Symbolic World Model

For some of the architectures under consideration ,

there (usually) exists a level that enables the

system to store knowledge in some basic

framework. This framework is based on symbols

which serve to represent relations between the

agent and its environment, and hence knowledge,

within the system. Such symbolic abstraction


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occurs at the symbol level and derives its power

from the physical symbol system hypothesis.

Thus, symbolic abstraction enables, or at least

facilitate, many architecture capabilities including

planning and learning. Soar's productions use

symbols to store information . Such systems have

memory ,symbols ,operations ,interpretation and

capacities to operate on symbols. [4]

3.2 Size Of The Knowledge Base

There are physical limits to the size of the

knowledge base that may be supported by an

individual agent. But, in addition to the physical

limit on memory , there may be limitations

imposed by the agent's architecture and style of

control. For example, Homer experiences a

processing slow-down as its episodic knowledge

base increases in size. Since productions can be

easily added to the knowledge base or learned in

Soar, the knowledge base may grow incrementally

to cover a wider range of behaviors. The ultimatesize of the knowledge base is dictated only by the

desired functionality; no absolute limits are made

by the architecture or the representation.

3.3 Glass Box Approach

Glass box knowledge representation may be

defined as the ability of rules to examine each

other. Glass box representation is useful for

modular architectures, so that all modules have

access to all knowledge. Glass box representation

allows the rules and the architecture to share

responsibility to examine, activate and rewards

other rules. Also, learning is facilitated by glass

box representation because learning modules may

write. Related properties are uniform access to

knowledge and homogeneous representation .

Knowledge in Prodigy is stored in a global

database which is freely available to all other

modules. Knowledge is represented uniformly to

simplify access by other modules. The declarative

representation of knowledge allows the

architecture to analyze its own actions and

decisions.

3.4 Black Box Approach

Black box knowledge representation may be

defined as the inability of rules to examine other

rules. Architectures with this property are limited

in their direct inferencing ability to see what other

rules are up to. Such representations are less

fragile to changes and more modular and easier to

understand.

Black-box knowledge representation does not rule

out meta-reasoning, but it would make it more

circuituitous. Some knowledge is shared amongst

all execution architectures, such as possible states

and actions. However, each architecture also

requires its own specific knowledge. This

knowledge includes utility values of actions and

states, which are not shared amongst architectures

in part because each architecture needs a different

type of utility value. Each execution architecture

operates independently and in parallel with the

others, with no interaction of the knowledge of

different architectures occurring. Each individual

execution architecture does not have its own meta-

knowledge in regards to what it knows. RALPH-

MEA uses this technique .

3.5 Declarative Representation

A declarative representation declares every

piece of knowledge and permits the reasoning

system to use rules of inference to come out with

a new piece of information .A classic example of a

declarative representation is logic. The primaryadvantages of declarative knowledge is the ability

to use knowledge in ways that the system designer

did not foresee. Often times, whether knowledge

is viewed as declarative or procedural is not an

intrinsic property of the knowledge base, but is a

function of what is allowed to read from it.

Production systems, for example, are declarative if

productions may view themselves, and are

procedural it they cannot. (cf. glass-box/black-box

control knowledge).

A particular architecture may use both

declarative and procedural knowledge at different

times, taking advantage of their differentadvantages. The architectures that use Declarative

representation are ERE , ICARUS , MAX ,

Prodigy and THEO . ERE uses Uniform

conceptual graph to store information . Homer ,

ICARUS , MAX and Theo use Frames . The

Prodigy use Prodigy description language .

3.7 Procedural Representation

Architectures with procedural representations

encode how to do some task. In other words,

procedural knowledge is skill knowledge. One

advantage of procedural representations is

possibly faster usage in a performance system.

Productions are a common means of representingprocedural knowledge.

Use of procedural knowledge in an agent

raises the questions of whether the agent can

"know what it knows" . That the agent can

demonstrate that it "knows what it knows" is

illustrated in a Soar system which includes the

ability to explain its actions. Procedural

representation is where knowledge is intrinsically

bound up in the routines and procedures which use

it . These procedures and routines know how to do


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a particular task which would be regarded as

intelligent .

3.8 Global Representation and Uniform Access to

Knowledge

In architectures with global knowledge,

different modules may read and/or write to

common database(s). Often this knowledge is

used to representing a world-view of what the

agent believes is true in its environment. An

advantage of having global knowledge is that

different modules may share their data and

abilities for more intelligent combined behavior.

This makes modular architectures more effective.

Also, such knowledge is necessary for

representing world-views: without it an

architecture may only react to its present sensor

readings (e.g. Subsumption Architecture.)[9].

A related property is knowledge

homogeneity, which is a measure of how similar

knowledge is represented by the architecture.

Homer's memory modules contains stateinformation which is globally available. The

planning module retrieves goals from the generic

memory. The interpreter and perception modules

enter events into memory. Although the world

state is uniformly accessible, other types of

knowledge in Homer is restricted

3.8 Efficiency of Knowledge Access

As the knowledge bases of these architectures

grow increasingly large, an efficient way of

accessing specific knowledge in the knowledge

bases becomes increasingly important. While this

is an issue for almost all architectures, MAX has

good and bad aspects for efficient knowledgeaccess. The good aspects are that all knowledge is

homogeneously represented and uniformly

accessed using frames. The bad aspects are that all

knowledge is declaratively stored.. In the case of

SOAR , with a very large knowledge base

containing perhaps a million or more productions,

accessing the knowledge in an efficient manner is

an important issue for the architecture. During the

Elaboration Phase in Soar, all the productions in

the memory that apply are fired in parallel. This

parallel operation allows the architecture to access

all the relevant knowledge in the long-term

memory in an efficient, tractable way.3.9 Knowledge Consistency

Knowledge consistency is the property that a

knowledge database contain no contradictions. It

is extremely important for knowledge

representations that may only either assert or deny

statements, with no measure of partial belief. One

such system is first order predicate calculus.

Because all statements may be either true or false,

it may be possible to store only part of the

statements , from which all true statements (or all

false statements) may be derived. Statements that

can't be derived from the basis set are assumed

false (or true). This is the closed-world

assumption. The primary advantage of knowledge

consistency is ability to store less statements.

An architecture that tolerates knowledge

inconsistency generally treats its knowledge base

as a set of competing hypotheses, or as a set of

statements that it has varying amounts of

confidence in. Often there is a numerical measure

of belief. The flexibility in representation and

flexibility in learning and reasoning are some of

the advantages .

Prodigy is a consistent knowledge

architecture while MAX is a inconsistent

knowledge architecture .

3.10 Homogeneous (Uniform) Knowledge

Representation

Architectures with global access to

knowledge may store it in a uniform format in a

central database, or may have it in a non-uniformformat in a distributed fashion. The uniform

method is often employed in systems with a

general knowledge representation scheme like

frames or first order predicate calculus. The non-

uniform method is often used in loosely-coupled

architectures for storing special knowledge not for

general use . Access all knowledge easily ,

interfaces not required to be changed by addition

or change of modules and easy to modify module

to accommodate new knowledge are the some of

the advantages of knowledge uniformity . The

design limitation , inefficiency to work on a few

modules and wastage of memory are some of theadvantages .

In Prodigy, all types of rules- inference rules,

operators, and control rules, are represented

uniformly. This allows the control rules to apply

to both inferences and operators, guiding the

application of rules. This representation allows

Prodigy to glass box approach.

3.11 Heterogeneous Knowledge Representation

The non-uniform method is often used in

loosely-coupled architectures for storing speciality

knowledge not for general use. A heterogeneous

knowledge representation is based on associatinga finite automaton with first order logic. A number

of knowledge representation problems raised by

the electromyography test features are examined

in this study and the expert system architecture

allowing such a knowledge modeling are laid out.

[6] .Entropy Reduction Engine uses

Heterogeneous Knowledge Representation .

3.12 No Explicit Representation

Usually the representation of knowledge

takes some form. Examples include: first-order


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predicate logic, frames, networks, scripts, etc.

However, knowledge does not have to be

represented explicitly. The subsumption and

ATLANTIS agents rely completely on the world

for information. There is no explicit knowledge

representation -- no global representation of state

nor any underlying symbol level. "The world is its

own best model"[7].

4 SPECIFIC EXAMPLES OF

REPRESENTATIONS AND MEMORY

STRUCTURES

4.1 Associative Memory

Associative memory refers to a memory

organization in which the memory is accessed by

its content . Thus, reference clues are "associated"

with actual memory contents until a desirable

match is found. Production systems are obvious

examples of systems that employ such a memory.Associative memory stands as the most likely

model for cognitive memories, as well. Humans

retrieve information best when it can be linked to

other related information. This linking is fast,

direct and labyrinthian in the sense that the

memory map is many-to-many and homomorphic

[8].

Associative Memory is a primary property of the

Soar .

4.2 Episodic Knowledge

Two particular types of knowledge --

procedural and declarative -- have been used

extensively in the design and development of

cognitive architectures. However, neither of these

types of knowledge characterize the knowledge

that humans use to remember events. Such

remembrances are called episodic knowledge.

Since this knowledge is, by definition,

experiential, it must learned by the agent rather

than pre-encoded (of course, it is possible to

conceive of episodic knowledge being pre-

encoded but, once running, additions to the

episodic memory would then be learned). This

knowledge can confer the capability to perform

protracted tasks or to answer queries about

temporal relationships and to utilize temporal

relationships.Episodic knowledge is a key feature of the Homer

architecture.

4.3 First-order Logic Representation

Many of the architectures analyzed build

upon a substrate of First Order Predicate Calculus.

This is a very descriptive declarative

representation with a well founded method of

deriving new knowledge from a database. Its

flexibility makes it a good choice when more than

one module may add to or utilize a common

database (c.f. Prodigy). Unfortunately, this

flexibility has limitations. First-order predicate

logic is composed of statements that are assumed

to be true. The statements are composed of atoms

,predicates, two substatements joined by a

conjunction, disjunction, or implication, a negated

substatement, and a statement with an existential

or universal quantifier (in this case, atoms in the

statements can be replaced by variables in the

quantifier). [1]

4.4 Strips-like Operator Representation

STRIPS, or the Stanford Research Institute

Problem Solver, was proposed by Fikes and

Nilsson in 1971[1] and included a representation

for operators that was intended to solve the frame

problem. STRIPS uses well-formed formulas of

the first-order predicate calculus and specifies

operators by a precondition list, an add-list and a

delete-list. The preconditions must be satisfied by

the current state before an operator is applied. Theeffects of the operator are given by the add and

delete lists. The add-list adds new, instantiated

well-formed formulas (or wffs, logical descriptions

of the world) to the current state. The delete-list

removes wffs from the current state.

Although STRIPS did resolve some of the

issues related to the frame problem, it (and all

systems that use a STRIPS-like representation)

suffers from a requirement for explicitness -- all

actions (including secondary effects) must be

included in the model of the operator. In complex

worlds, this is often impossible [10].

4.5 Frame-Like RepresentationMarvin Minsky in the book on computer

vision proposed frames as means of representing

common-sense knowledge. Here knowledge is

organized in to small packets called frames. The

contents of the frame are certain slots which have

values.

To example the concept of frames, try to

recall the structure of our computer centre.

Various equipments like computer system, dump

terminals, printers, air-conditions, un-interrupted

power supplies etc. are placed at some position in

the computer centre. By this time we have formed

an image of computer centre in mind and it iseasy for us to identify without going there where

things are kept. Suppose we visit a new computer

center. Our mind now considers a series of

permutation and combinations . The frame of the

computer centre in mind is oriented towards the

present situation with some slots being added (for

new objects) and some slots being removed. For

every equipment /object, we form a frame.

A frame can be defined as a data structure that

has slots for various objects and a collection of


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frames consists of various objects and a collection

of frames consists of expectations for a give

situation. A frame structure provides facilities for

describing objects, facts about situations,

procedures in what to do when a situation is

encountered. Because of there facilities a fames

provides, frames are used to represent the two

types of knowledge, namely declarative factual

and procedural. Like a semantic network, one of

the chief properties of frames is that they provide

a natural structure for inheritance. ISA Links

connect classes to larger parent classes and

properties of the subclasses may be determined at

both the level of the class itself and from parent

classes.

In addition, the values of a particular attribute

need not necessarily be filled with a value but may

also indicate a procedure to run to obtain a value.

This is known as an attached procedure. Attached

procedures are especially useful when there is a

high cost associated with computing a particularvalue, when the value changes with time or when

the expected access frequency is low. Instead of

computing the value for each instance, the values

are computed only when needed. However, this

computation is run during execution (rather than

during the establishment of the frame network)

and may be costly.

THEO , Homer , Meta Reasoning

Architecture extensively use frames for

representation .

In the Table1A and Table 1 B , Rows indicate the

Memory , Knowledge and knowledgerepresentation and the column indicates the

particular architecture . Y in the cell means that

the architecture corresponding to column has the

type of memory or knowledge and knowledge

representation .

Table 1A

Memory,Knowledge

&

Representation

Subsumption

ATLANTIS

THEO

PRODIGY

ICARUS

ADAPTIVEINTELLIGENT

SYSTEMS

Forward &Back-ward Y Y

Chaining

Impasse-drivenControl Y YSerial

Processing Y YParrellelProcessing Y Y

AsynchronousProcessing Y YInterruptibleProcessing Y YOpen-Loopprocessing Y

Closed -LoopProcessing Y

HierarchicalOrganization Y Y Y YModularOrganization Y Y Y Y

SymbolicWorldModel Y Y Y Y Y

Size of theKnowledge -Base Y YGlass Box

approach Y YBlack BoxApproach Y Y

DeclarativeRepresentation Y Y YProceduralRepresentationGlobalRepresentation Y Y Y Y

Uniform

Access toKnowledge Y YKnowledgeConsistency Y YHomogenous ( Uniform)KnowledgeRepresentation Y Y Y Y


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HeterogeneousKnowledgeRepresentationNo-Explicit

Representation Y

AssociateMemory

EpisodicKnowledgeMeta-Knowledge YFirst-OrderLogicRepresentation Y

STRIPS-LIKEOPERATORREPRESENTATION Y

Frame -LikeRepresentation YNetworkrepresentation Y Y

Table 1B

Memory,Knowledge&

Representation

METAREASONING

ARCHITECTURE

HOMER

SOAR

TETON

RALPH-MEA

ENGINE

Forward &Back-wardChaining YImpasse-drivenControl Y Y YSerialProcessing Y Y Y Y

ParallelProcessing Y Y Y

AsynchronousProcessing Y

InterruptibleProcessing Y Y

Open-Loopprocessing Y

Closed -LoopProcessingHierarchicalOrganization Y Y

ModularOrganization Y Y Y YSymbolicWorld Model Y Y Y Y YSize of theKnowledge -Base YGlass Boxapproach Y Y

Black BoxApproach Y Y YDeclarativeRepresentation Y Y YProceduralRepresentation Y Y

GlobalRepresentation Y Y Y Y Y YUniformAccess to

Knowledge Y YKnowledgeConsistency Y

Homogenous( Uniform)KnowledgeRepresentation Y

HetrogenousKnowledgeRepresentation Y Y YNo-Explicit

RepresentationAssociateMemory YEpisodicKnowledge Y

Meta-Knowledge Y Y Y YFirst-OrderLogicRepresentation


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STRIPS-LIKEOPERATORREPRESENTATION Y YFrame - LikeRepresentatio

n Y YNetworkrepresentation

Table 2

Sl

N

o

Architect

ure

Features

1 Subsumpt

ion

Complicated Intelligent

behavior

Into

Simple behavior modules

Organized into

layers

2 THEO Plan-Then-Complie

By this

Integrating learning ,

planning and knowledge

representation

3 ICARUS Specific representation of

long term memory .It uses

3 independent

asynchronous modules

responsible for

1.Perception

2.Planning3.Effecting

4 PRODIG

Y

Storing the knowledge in a

form of first order predicate

logic (FOPL) called

Prodigy Description

language (PDL) .

Has a modular architecture

that stores the knowledge

symbolically.

5 ATLANT

IS

Integrating planning and

reacting in a heterogeneous

asynchronous architecture

for mobile agents .

It consists of 3 layers:

1.Control layer

2.sequencing layer

3.deliberative layer

6 Adaptive

Intelligen

t System

(AIS)

To reason about and

interact with other dynamic

entities in real time .

--- problem solving

techniques

--- when encountering un-

expected situation , decides

whether to and how to

respond .

--- focus attention on the

most critical aspects of

current situation .

--- operating continuously

without rebooting .

--- able to coordinate with

external agent .

(more or less similar to

human being )

7 Meta

Reasonin

g (MAX)

Many ideas in MAX may

traced to Prodigy.

--- rule-based forward

chaining engine that

operates on productions .

--- is designed to support to

modular agents.

--- they are used to

respond to a dynamicenvironment in a timely

manner .

--- modules are categorized

in to Behavior and monitor

.

--- Some of the modules

are:

1.attention focusing

2.multiple problem solving

strategies

3.execution monitoring

4.goal-directed exploration5.explantion-based learning

6.process interruption

7.intelligent resumption

8 HOMER --- Is not designed for

general intelligence .

--- underlying philosophy is

to synthesize several key

areas of AI to form one

complete system . (like

planning, learning , natural

language understanding ,

robotic navigation ) .

HOMER answers questionsposed by users and carries

out instructions given by

users .

--- is a modular structure.

It consists of :

1.memory

2.planner

3.natural language

interpreter and generator .

4.reflective processes


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5.plan executer

9 SOAR --- originally known as

STATE OPERATOR AND

RESULT .

--- main goal is that full

range of capabilities to behandled by an intelligent

agent from highly routines

to extremely difficult open-

problems

--- the underlying SOAR

architecture is the view that

symbolic system is

necessary and sufficient

condition for general

intelligence . This is known

as Physical Symbolic

system Hypothesis (PSSH)

--- ultimate aim is to get

general intelligent agent--- is based on a production

system ie. It uses explicit

production rules to govern

its behaviors .

10 Teton --- is a problem solver

--- uses two memory areas

1.Short-Term

memory

2. Long-Term

memory

--- like human beings ,interruption are allowed .

--- it has a feature called

Execution Cycle which

always look for what to do

next .11 RALPH-

MEA

--- is a multiple execution

architecture

--- like human being ,

selecting best one from the

environment

--- RALPH MEA uses

Execution Architecture

(EA) to select from onestate to best one .

--- it uses the following :

1.Condition action

2.Action utility

3.Goal based

4.Decision Theoretic

12 Entropy

Reductio

n Engine

(ERE)

--- focuses on problems

that require planning ,

scheduling and control

--- uses many different

problem solving methods

such as :

1.problem reduction

2.temporal projection

3.rule-based execution

5 CONCLUSION

The problem of AI is to describe and build agents

that receive percepts from the environment and

perform actions, and each such agent is

implemented by a function that maps percepts to

actions. It explains the role of learning as

extending the reach of the designer into unknown

environments, and shows how it constrains agentdesign, favoring explicit knowledge representation

and reasoning . It analyzes basic techniques for

addressing complexity . The idea is to Integrate

state-of-the art AI techniques into intelligent agent

designs, using examples from twelve agents to

full knowledge-based agents with natural

language capabilities and so on . This leads to the

study of Multi-Agent systems and its applications

. In depth analysis of various Agent architectures

and their capabilities is to build a Multi Agent

System that will be suitable for our future work on

Supply Chain Management

REFERENCE

[1] Elavine Rich , Kevin Knight and Shivshankar

, Artificial Intelligence by The Mcgraw Hill

Publishing Company limited .

[2] Kurt Vanlehn , Rule-Learning Events in the

acquisition of a complex skill ,

Journal article by, Journal of the Learning Science

,vol 8 , 1999

[3] Philip E. Agre , Hierarchichy and History in

Simons Architecture of Complexity Journal of

Learning Science 12(3) , 2003

[4] Newell a , Unified theories of Cognition ,

Harvad University Press , Cambridge ,

Massachusetts , 1990

[5] Levesque H , Brachman R , A fundamental

trade off in Knowledge representation and

reasoning , 1998

[6] Vincent Rialle , Annick Vila , Yves Besnard ,

Artificial Intelligence in Medicine 1991

[7] Vincent C.Muller , Is there a future for AI

without representation , springer Verlag , 2007

[8] Pat Langley , Dongkyu Choi , Seth Rogers ,

Interleaving Learning Laboratory Centre for the


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study and languages and information , Stanford

University , Stanford .

[9] A.S Maida , A Uniform architecture for rule-

based meta-reasoning and representation :- case

study . , IEEE Computer Society Press.

[10] Artificial Intelligence: Modern Approach by

Stuart J. Russell , Peter Norvig Prentice HallSeries in Artificial Intelligence.

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