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Solution Manual to Third Edition AI

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ABOUT THE AUTHORS

Elaine Rich is Director of the Artificial Intelligence Laboratory at the Microelectronics

and Computer Technology Corporation (MCC). Formerly on the faculty in computer sciences

at the University of Texas, she received her PhD from Carnegie Mellon University. Her

research interests include natural language processing. knowledge representation, and

machine translation.

Kevin Knight received his B. from Harvard University and is presently completing his

PhD in computer science at Carnegie Mellon University. A regular consultant at MCC, his

research interests include natural language processing, unification, and machine translation,

and search.

Shivashankar B Nair received his Master’s and Doctoral degrees in Engineering from

Amravati University, Amravati, where he served as a faculty member from 1986 to 1998.

He later joined the Indian Institute of Technology Guwahati, where he is currently an

Associate Professor in Computer Science and Engineering. His major areas of interest include

real-world Artificial Immune System Applications, Intelligent and Emotional Robotics,

Natural Language Processing, Genetic Algorithms and Mobile Agent Systems.

Dr Nair has been the chief investigator for projects funded both by the Indian government

and foreign agencies and is a member of several international and national journal and

conference committees. Presently, he is on a sabbatical as a Visiting Professor (Korean

Brain Pool) at the Human-Centred Advanced Research Education Centre, Hanbat National

University, Daejeon, South Korea, where he is investigating new ways of instilling emotions

into robots.

viii Contents

4 Issues in Knowledge Representation
4 The Frame Problem
Summary
1. Using Predicate Logic
5 Representing Simple Facts in Logic
5 Representing Instance and ISA Relationships
5 Computable Functions and Predicates
5 Resolution
5 Natural Deduction
Summary
Exercises
1. Representing Knowledge Using Rules
6 Procedural Versus Declarative Knowledge
6 Logic Programming
6 Forward Versus Backward Reasoning
6 Matching
6 Control Knowledge
Summary
Exercises
1. Symbolic Reasoning Under Uncertainty
7 Introduction to Nonmonotonic Reasoning
7 Logics for Nonmonotonic Reasoning
7 Implementation Issues
7 Augmenting a Problem-solver
7 Implementation: Depth-first Search
7 Implementation: Breadth-first Search
Summary
Exercises
1. Statistical Reasoning
8 Probability and Bayes’ Theorem
8 Certainty Factors and Rule-based Systems
8 Bayesian Networks
8 Dempster-shafer Theory
8 Fuzzy Logic
Summary
Exercises
1. Weak Slot-and-Filler Structures
9 Semantic Nets
9 Frames
Exercises
1. Strong Slot-and-Filler Structures
10 Conceptual Dependency
10 Scripts
10 CYC
Exercises
1. Knowledge Representation Summary
11 Syntactic-semantic Spectrum of Representation
11 Logic and Slot-and-filler Structures
11 Other Representational Techniques
11 Summary of the Role of Knowledge
Exercises
1. Game Playing PART III:ADVANCED TOPICS
12 Overview
12 The Minimax Search Procedure
12 Adding Alpha-beta Cutoffs
12 Additional Refinements
12 References on Specific Games
Exercises
1. Planning
13 Overview
13 An Example Domain: The Blocks World
13 Components of a Planning System
13 Goal Stack Planning
13 Nonlinear Planning Using Constraint Posting
13 Hierarchical Planning
13 Reactive Systems
13 Other Planning Techniques
Exercises
1. Understanding
14 What is Understanding?
14 What Makes Understanding Hard?
14 Understanding as Constraint Satisfaction
Summary
Exercises
1. Natural Language Processing
15 Introduction
15 Syntactic Processing
15 Semantic Analysis
20 Explanation
20 Knowledge Acquisition
Summary
Exercises
1. Perception and Action
21 Real-time Search
21 Perception
21 Action
21 Robot Architectures
Summary
Exercises
1. Fuzzy Logic Systems
22 Introduction
22 Crisp Sets
22 Fuzzy Sets
22 Some Fuzzy Terminology
22 Fuzzy Logic Control
22 Sugeno Style of Fuzzy Inference Processing
22 Fuzzy Hedges
22 A Cut Threshold
22 Neuro Fuzzy Systems
22 Points to Note
Exercises
1. Genetic Algorithms: Copying Nature’s Approaches
23 A Peek into the Biological World
23 Genetic Algorithms (GAS)
23 Significance of the Genetic Operators
23 Termination Parameters
23 Niching and Speciation
23 Evolving Neural Networks
23 Theoretical Grounding
23 Ant Algorithms
23 Points to Ponder
Exercises
1. Artificial Immune Systems
24 Introduction
24 The Phenomenon of Immunity
24 Immunity and Infection
24 The Innate Immune System—The First Line of Defence
24 The Adaptive Immune System—The Second Line of Defence
24 Recognition
24 Clonal Selection
24 Learning
24 Immune Network Theory
24 Mapping Immune Systems to Practical Applications
24 Other Applications
24 Points to Ponder
Exercises
Glossary of Biological Terms Used
1. Prolog—The Natural Language of Artificial Intelligence
25 Introduction
25 Converting English to Prolog Facts and Rules
25 Goals
25 Prolog Terminology
25 Variables
25 Control Structures
25 Arithmetic Operators
25 Matching in Prolog
25 Backtracking
25 Cuts
25 Recursion
25 Lists
25 Dynamic Databases
25 Input/Output and Streams
25 Some Aspects Specific to LPA Prolog
Summary
Exercises
1. Conclusion
26 Components of an AI Program
26 Ai Skeptics—An Open Argument
Exercises
References
Author Index
Subject Index

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