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Overview of the Second Edition of Modern Concept in Artificial Intelligence by M. S. Okundamiya
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PREFACE TO THE FIRST EDITION
Modern Concepts in Artificial Intelligence is primarily intended for use in an
undergraduate course. Due to its comprehensive coverage and a number of detailed
algorithms, it is useful as a primary reference volume for Artificial Intelligence (AI)
graduate students and professionals wishing to branch out beyond their own subfield.
The only prerequisite is familiarity with basic concepts of computer science
(algorithms, data structures, complexity, etc.) at a sophomore level. AI is a big field;
the main unifying theme is the idea of an intelligent agent. In this book AI is defined as
the science and engineering of making intelligent agents. Each such agent implements a
function that maps percept sequences to actions.
The book is divided into five chapters. Chapter one which introduces the course offers
an overview of AI and traces the history, from the gestation through the emergence of
intelligent agents. Chapter two centred on intelligent agents: systems that can decide
what to do and then do it, and compares the two approaches – human centred and
rationalist – in determining the intelligence of and intelligent agent. Chapter three
focuses on the essential concepts of expert systems and knowledge engineering as it
relates to AI while chapter four considers the commonly used programming languages
for AI: Prolog and Lisp; stating the basic concepts and essentials of programming
through the use of carefully chosen sample programs. The sample programs are
arranged to guide the student through the development of Lisp and Prolog programs
that are constructed in a top-down, declarative fashion which also gives an outlined
view of the major built-in predicates.
ii Modern Concepts in Artificial
Chapter five describes that part of the intelligent agent responsible for reaching
decisions, ways to represent knowledge about the world--how it works, what it is
currently like, and what one's actions might do--and how to reason logically with that
knowledge; examining the fundamental concepts of logical representation and
reasoning. It also considers constraint satisfaction problems, which provides a natural
connection to the material on logic; propositional logic, which was presented as a
stepping-stone to first-order logic as well as the natural language processing
M. S. Okundamiya
February, 2009.
iiiM. S. Okundamiya
PREFACE TO THE SECOND EDITION
As a result of further research undertaken by the author it has now been found possible
to provide a new edition of Modern Concepts in Artificial Intelligence incorporating
additional topics and three new chapters for wider undergraduate course coverage in
science and engineering.
The additional topics and three new chapters in this second edition are as follows: the
inclusion of classes of intelligent agents and problem solving as search for goal-based
agents, and symbolic and non-symbolic representations respectively to existing chapter
two and three. Chapter six considers fundamental concepts, methods and algorithms of
constraint satisfaction problems, which provide a natural connection to the material on
logic. Chapter seven deals with natural language processing while chapter eight
presents general overview of genetic programming comparing, its attributes with other
approaches of artificial intelligence. Advantage has also been taken during the revision
of the book to a number of minor points in other parts of the text as well as changing
the format and resetting of the whole pages of the text to provide improved learning
potential for the reader.
I sincerely appreciate God Almighty for His infinite mercy, wisdom, favour and
protection over my family. I would also express my gratitude to the reviewer(s) for
their inputs to this edition. The author would welcome your suggestions, feedback,
criticism or any other comments that will enable me improve the next edition of
Modern Concepts in Artificial Intelligence. Please, send your comments to
M. S. Okundamiya
May, 2011.
iv Modern Concepts in Artificial
CONTENTS
Preface iii
Dedication vi
Contents vii
CHAPTER ONE: INTRODUCTION
1.1 General Overview 1
1.1.1 Artificial Intelligence as Science 3
1.1.2 Artificial Intelligence as Engineering 4
1.2 Goals of Artificial Intelligence 5
1.3 History of Artificial Intelligence 6
1.3.1 The Gestation of Artificial Intelligence (1943-1956) 7
1.3.2 Early Enthusiasm, Great Expectations (1952-1969) 8
1.3.3 A Dose of Reality (1966-1974) 9
1.3.4 Knowledge-Based Systems: Key To Power (1969-1979) 9
1.3.5 Ai Becomes an Industry (1980-Present) 10
1.3.6 Ai Becomes A Science (1987-Present) 11
1.3.7 The Emergence of Intelligent Agents (1995 - Present) 12
1.4 Branches of Artificial Intelligence 12
1.5 Applications of Artificial Intelligence 21
CHAPTER TWO: INTELLIGENT AGENT
2.1 Overview 24
2.2 Acting Humanly: The Turing Test Approach 28
2.3 Thinking Humanly: The Cognitive Modelling Approach 29
2.4 Acting Rationally: The Rational Agent Approach 30
vM. S. Okundamiya
2.5 Thinking Rationally: The Laws of Thought Approach 31
2.6 Agents and Environment 33
2.6.1 General Assumption 33
2.6.2 Properties of Task Environments 35
2.7 Classes of Intelligent Agents 37
2.7.1 Simple Reflex Agents 37
2.7.2 Model-Based Reflex Agents 37
2.7.3 Learning Agents 38
2.7.4 Goal-Based Agents 39
2.7.5 Utility-Based Agents 41
2.8 Problem Solving as Search 41
2.9 State of the Art 46
CHAPTER THREE: EXPERT SYSTEMS
3.1 Overview 49
3.2 Knowledge Engineering 50
3.3 Expert System Shells 51
3.3.1 Tree-Based Logic 53
3.3.2 Forward Chaining 53
3.3.3 Backward Chaining 55
3.3.4 Bayesian Belief Networks 58
3.3.5 Neural Networks 59
3.3.6 Fuzzy Logic 59
3.3.7 State Machines 61
3.3.8 Case-Based Reasoning (CBR) 62
3.3.9 Object Oriented Design 62
vi Modern Concepts in Artificial
3.4 The Building Blocks of Expert Systems 63
3.4.1 Knowledge Base 63
3.4.2 Inference Engine 64
3.4.3 Interface 64
3.5 Applications of Expert Systems 65
3.6 Benefits to End Users 67
CHAPTER FOUR: INTRODUCTION TO LISP AND PROLOG
4.1 List Processing 69
4.1.1 Definitions 69
4.1.2 Basic Functions 70
4.1.3 How to Write Functions 72
4.1.4 The Logic of Functions 73
4.1.5 Built-In Functions 77
4.2 Programming in Logic 81
4.2.1 PROLOG Data Structures 83
4.2.2 Unification 86
4.2.3 Operators 87
4.2.4 Applications of PROLOG 88
CHAPTER FIVE: LOGICAL AGENTS
5.1 Introduction 89
5.2 Knowledge-Based (KB) Agents 90
5.3 Logic 96
5.4 Symbolic and Non-Symbolic Representations 102
5.5 Propositional Logic 104
viiM. S. Okundamiya
5.5.1 Syntax 104
5.5.2 Semantics 107
5.5.3 Inference 110
5.5.4 Equivalence, Validity, and Satisfiability 111
5.5.5 Effective Propositional Inference 114
CHAPTER SIX: CONSTRAINT SATISFACTION PROBLEMS
6.1 Introduction 118
6.1.1 Standard Search Formulation 118
6.1.2 Varieties of Constraint Satisfaction Problems 119
6.1.3 Varieties of Constraints 120
6.1.4 Real-World Constraint Satisfaction Problems 122
6.2 Algorithms for Constraint-Satisfaction Problems 122
6.2.1 Constraint Propagation 123
6.2.2 Backtracking 123
6.2.3 Local Search 125
6.3 Intelligent Backtracking and Truth Maintenance 126
6.4 Variable Ordering and Value Instantiation 127
CHAPTER SEVEN: NATURAL LANGUAGE PROCESSING
7.1 Introduction 131
7.2 Natural Language 131
7.2.1 Significance of Natural Language Study 132
7.2.2 Areas of Natural Language 133
7.2.3 Minimality of Natural Language 134
7.3 Computer Language Understanding 134
viii Modern Concepts in Artificial
7.4 Problems in Understanding Language 135
7.5 Natural Language as Artificial Intelligence Problem 136
7.6 Applications 136
CHAPTER EIGHT: INTRODUCTION TO GENETIC PROGRAMMING
8.1 Introduction 138
8.2 Genetic Operators 139
8.3 Operational Principles 139
8.4 Differences between Genetic Programming and other Approaches to
Machine Learning and Artificial Intelligence 140
8.5 Attributes of a system for Automatic Programming 150
8.6 The Human-Competitive Results 153
8.6.1 An Automatically Created Solution to a Problem is Competitive with
Human-Produced Results 154
8.6.2 Criteria for an Automatically Created Result to be Human-Competitive 154
References 156Index 170
REFERENCE
Allen, J. 1983. Maintaining knowledge about temporal intervals, Communications
of the ACM, (26):832–843
Allen, J. 1984. Toward a general theory of action and time, Artificial Intelligence,
3(2): 123–154.
Allen, J. F., 1995. Natural language understanding, Benjamin/Cummings, Redwood
City, California
Amarel, S. 1968. On representations of problems of reasoning about actions, In
Michie, D. (Ed.), Machine Intelligence 3; Elsevier/North-Holland, 3:131-
171.
Andersen, S. K, et al. 1989. HUGIN - a shell for building Bayesian belief universes
for expert systems. In proceedings of the 11th International Joint Conference
on Artificial Intelligence, Detroit, Morgan Kaufman2:1080 -1085
Art, K.R., 1999. The essence of constraint propagation, Theoretical computer
science, 221 (1-2): 179-210
Ashby, W. R., 1948. Design for a brain. Electronic Engineering Wiley, NY. pp 379-
383.
Bartak, R. 2001. Theory and practice of constraint propagation, In proceeding of the
3rd Workshop on Constraint Programming for Decision and Control,
Gliwice, Poland. pp. 7-14
Barto, A. G. et al. 1995. Learning to act using real-time dynamic programming,
Artificial Intelligence, 73 (1): 81-138
157M. S. Okundamiya
Bell, J. L. and M. Machover, 1977. A course in mathematical logic, Elsevier, North-
Holland
Bellman, R. E., 1978. An Introduction to Artificial Intelligence: Can Computers
Think? San Francisco: Boyd & Fraser.
Berliner, H. J. 1980. Backgammon computer program beats world champion.
Artificial Intelligence, 14: 205-220.
Bernardinis, L. A. 1993. Clear thinking on Fuzzy Logic, Machine Design.
Bernardo, J. M and A. F. M. Smith, 1994. Bayesian Theory, Wiley NY.
Bernstein, A and M. Roberts, 1988. Computer vs. chess player. Scientific
American, 198 (6): 96-105
Bertoli, P. et al. 2001. Heuristic search + symbolic model checking = efficient
informant planning. In proceeding of the 17th International Joint Conference
on Artificial Intelligence, Seatle; Morgan Kaufmann. pp 467-472
Bertsekas, D., 1987. Dynamic programming: Determinstic and stochastic models.
Prentice-Hall, Upple Saddle River, New Jersey
Bitner J. R. and E. M. Reingold, 1975. Becktrack programming techniques,
Communication of the Association for Computing Machinery, 18(11): 651-
656
Bobrow, D. G. and B. Raphael, 1974. New programming languages for Artificial
Intelligence Researcher, Computing Surveys 6 (3): 153-174
Boden, M. A. (Ed.) 1990. The philosophy of Artificial Intelligence, Oxford
University Press, Oxford UK.
Boden, M. A., 1977. Artificial Intelligence & natural man, Basic books, NY
158 Modern Concepts in Artificial
Boyer, R. S. and J. S. Moore, 1979. A computational logic, Academic Press, NY.
Bratko, I., 1986. Prolog Programming for Artificial Intelligence, 1st Ed. Addison-
Wesley. Reading Massachusetts.
Bratko, I. 2001. Prolog Programming for Artificial Intelligence, 3rd Ed. Addison-
Wesley. Reading Massachusetts.
Briggs, R. 1985. Knowledge representation in Sanskrit and Artificial Intelligence,
AI Magazine, 6 (1): 32-39
Broadbent, D. E 1958. Perception and Communication, Pergamon Oxford, UK.
Bruynooghe, M. 1981. Solving combinatorial search problems by Intelligent
Backtracking, Information Processing Letters 12(1): 36–39.
Bruynooghe, M., and L. M. Pereira, 1984. Deduction revision by Intelligent
Backtracking. In Implementations of Prolog, (Ed.) J. A. Campbell, 194–215,
Chichester, England: Ellis Horwood.
Buchanan B. G. and E. H. Shortliffe, (Eds.) 1984. Rule-Based Expert Systems: The
MYCIN experiments of the Standford Heuristic Programming Project,
Addison-Wiley.
Chakravarty, I. 1979. A generalized line and junction labelling scheme with
applications to scene analysis, IEEE Transactions on Pattern Analysis and
Machine Intelligence 1(2): 202–205.
Charniak, E. and D. McDermott, 1985. Introduction to Artificial Intelligence, MA:
Addison-Wesley.
Clark, K. L. 1978. Negation as failure, In Gallaire, H. and J. Minker (Eds.), Logic
and Data Bases, Plenum, NY. pp 293-322
159M. S. Okundamiya
Clocksin W. F. and C. S. Mellish 1994. Programming in Prolog, 4th Ed., Springer-
Wesley, Berlin
Cohen J. 1988. A view of the origins and development of prolog, Communication
of the Association for Computing Machinery, 31: 26-36
Cox, E. 1992. Fuzzy Fundamentals, IEEE Spectrum, pp. 58-61
Davis, A. L., and A. Rosenfeld, 1981. Cooperating processes for low-level vision:
A survey. Artificial Intelligence 17:245–263.
Davis, R. and D. B. Lenat, 1982. Knowledge – based systems in Artificial
Intelligence. McGraw Hill, NY.
De Kleer, J. 1986a. An assumption-based TMS, Artificial Intelligence 28:127–162.
De Kleer, J. 1986b. Problems with ATMS, Artificial Intelligence 28:197–224.
De Kleer, J. 1989. A comparison of ATMS and CSP techniques, In proceedings of
the Eleventh International Joint Conference on Artificial Intelligence, Menlo
Park, Califonia, 290-296.
De Kleer, J., and G. J. Sussman, 1980. Propagation of constraints applied to circuit
Synthesis. Circuit Theory and Applications 8:127–144.
Dechter, R. 1986. Learning while searching in constraint-satisfaction problems, In
proceedings of the Fifth National Conference on Artificial Intelligence,
Menlo Park, Califonia, 178–183.
Dechter, R. 1987. A constraint-network approach to truth maintenance, Technical
Report, R-870009, Cognitive Systems Laboratory, Computer Science Dept.,
Univ. of California at Los Angeles
Dechter, R. 1990. Enhancement schemes for constraint processing: Backjumping,
Learning, and Cutset Decomposition. Artificial Intelligence 41(3): 273–312.
160 Modern Concepts in Artificial
Dechter, R. and D. Frost, 1999. Backtracking algorithm for CSPs Tech. Rep., Dept.
of Information and Computer Science, University of California, Irvine.
Dechter, R., and I. Meiri, 1989. Experimental evaluation of preprocessing
techniques in constraint-satisfaction problems. In proceedings of the
Eleventh International Joint Conference on Artificial Intelligence, Menlo
Park, Califonia, 290–296.
Dechter, R., and J. Pearl, 1988a. Network-based heuristics for constraint-
satisfaction problems. Artificial Intelligence 34:1–38.
Dechter, R., and J. Pearl, 1988b. Network-Based Heuristics for Constraint-
Satisfaction Problems. In Search in Artificial Intelligence, (Eds.) Kanal, L.
and V. Kumar, New York: Springer-Verlag, 370–425.
Dechter, R., I. Meiri, and J. Pearl, 1990. Tree Decomposition with Applications to
Constraint Processing. In Proceedings of the Eighth National Conference on
Artificial Intelligence, Menlo Park, Califonia, 10–16.
Dennett, D. 1998. Brainchildren: Essays on designing minds. MIT Press.
Dhar, V., and A. Croker, 1990. A Knowledge Representation for Constraint-
Satisfaction Problems, Technical Report, 90-9, Dept. of Information
Systems, New York Univ.
Dhar, V., and N. Ranganathan, 1990. Integer Programming versus Expert Systems:
An Experimental Comparison. Communications of the ACM 33:323–336.
Doyle, J. 1979. A truth maintenance system, Artificial Intelligence 12:231–272.
Eastman, C. 1972. Preliminary report on a system for general space planning.
Communications of the ACM 15:76–87.
161M. S. Okundamiya
Evans, T. G. 1968. A program for the solution of a class of geometric-analogy
intelligence-test questions. In Minsky, M. L. (Ed.), Semantic Information
Processing, MIT Press, 271–353.
Fagin, R. et al. 1995. Reasoning about knowledge, MIT Press, Cambridge.
Feignebaum, E.A. and J. Feldman, (Eds.) 1963. Computers and thought, McGraw
Hill, NY.
Feldman, R., and M. C. Golumbic, 1989. Constraint satisfiability algorithms for
interactive student scheduling. In proceedings of the Eleventh International
Joint Conference on Artificial Intelligence, Menlo Park, California, 1010–
1016.
Fischer, M. J. and R. E. Ladner, 1977. Propositional modal logic of programs, In
proceeding of the 9th ACM symposium on the theory of computing, ACM
press NY, 286-294
Fogel, L. J., A. J. Owens and M. J. Walsh, 1966. Artificial Intelligence through
Simulated Evolution. Wiley.
Fox, M. S. 1987. Constraint-Directed Search: A case study of job shop scheduling.
San Mateo, Calif.: Morgan Kaufmann.
Fox, M. S., N. Sadeh, and C. Baykan, 1989. Constrained heuristic search. In
proceedings of the Eleventh International Joint Conference on Artificial
Intelligence, Menlo Park, California, 309–315.
Frayman, F. and S. Mittal, 1987. COSSACK: A constraint-based expert system for
configuration tasks. In Knowledge-Based Expert Systems in Engineering:
Planning and Design, (Eds.) Sriram D. and R. A. Adey, Billerica, Mass.:
Computational Mechanics Publications, 143–166.
162 Modern Concepts in Artificial
Freuder, E. and M. Quinn, 1985. Taking advantage of stable sets of variables in
constraint-satisfaction problems, In proceedings of the Ninth International
Joint Conference on Artificial Intelligence, Menlo Park, California, 1076–
1078.
Gaschnig, J. 1977. A General Backtrack Algorithm That Eliminates Most
Redundant Tests. In proceedings of the Fifth International Joint Conference
on Artificial Intelligence, Menlo Park, California, 457–457.
Geffner, H. and J. Pearl, 1987. An improved constraint-propagation algorithm for
diagnosis. In proceedings of the Tenth International Joint Conference on
Artificial Intelligence, Menlo Park, California, 1105–1111.
Ginsberg, M. L. 1993. Essentials of Artificial Intelligence. Morgan Kaufruann San
Mateo, California.
Gockett, L. 1994. The Turning test and the frame problem: A1’s mistaken
understanding of intelligence. Ablex, Norwood, New Jersey.
Gu, J. 1989. Parallel algorithms and architectures for very fast AI Search. Ph.D.
dissertation, Computer Science Dept., Univ. of Utah.
Haralick, R. and G. Elliot, 1980. Increasing tree search efficiency for constraint-
satisfaction problems. Artificial Intelligence 14(3): 263–313.
Harnad, S. 1991. Other bodies, other minds: A machine incarnation of an old
philosophical problem, Minds and Machines, Kluwer Academic Publishers
1: 43-54.
Haugeland, J. (Ed.) 1981. Mind Design. MIT Press, Cambridge.
Haugland, J., (Ed.) 1985. Artificial Intelligence: The Very Idea. Cambridge, MA:
MIT Press
163M. S. Okundamiya
Holland, J. H. 1975. Adaption in natural and artificial systems. University of
Michigan Press.
Hopfield, J. J. 1982. Neurons with graded response have collective computational
properties like those of two-state neurons. PNAS, 79: 2554–2558.
Hummel, R. A., A. Rosenfeld, and S. W. Zucker, 1976. Scene labelling by
relaxation operations. IEEE Transactions on Systems, Man, and Cybernetics
6(6): 420–433.
Jurafsky, D. and M. James, 2000. Speech and language processing, Prentice-Hall,
Kale, L. V. 1990. A perfect heuristic for the n non-attacking queens problem.
Information Processing Letters 34(4): 173–178.
Kasabov, N. 1998. Introduction: Hybrid intelligent adaptive systems. International
Journal of Intelligent Systems, 6:453-454.
Kirkpatrick, S., C. D. Gelatt and M. P. Vecchi, 1983. Optimization by simulated
annealing. Science, 220, 671–680.
Kolmogorov, A. N. (1950). Foundations of the Theory of Probability. Chelsea.
Koza, J. R., F. H. Bennett III, D. Andre, and M. A. Keane, 1999. Genetic
Programming III: Darwinian Invention and Problem Solving, Morgan
Kaufmann
Koza, J. R., M. A. Keane, M. J. Streeter, W. Mydlowec, J. Yu, and G. Lanza, 2003.
Genetic Programming IV: Routine Human-Competitive Machine
Intelligence, Kluwer Academic Publishers
Kumar, V. 1987. Depth-first search. In Encyclopaedia of Artificial Intelligence,
(Ed.) S. C. Shapiro, New York: Wiley, 2:1004–1005.
164 Modern Concepts in Artificial
Kumar, V., and Y. Lin, 1988. A data-dependency-based intelligent backtracking
scheme for Prolog. The Journal of Logic Programming 5(2): 165–181.
Kurzweil, R. 1990. The age of intelligent Machines. Cambridge, MA: MIT Press.
Lee, C.C. 1990. Fuzzy Logic in Control Systems, IEEE Trans. on Systems, Man,
and Cybernetics, SMC, 20 (2): 404-35.
Lenat, D. B. 1983. EURISKO: A program that learns new heuristics and domain
concepts: The nature of heuristics, III: Program design and results. AIJ,
21(1–2), 61–98.
Lighthill, J. 1973. Artificial intelligence: A general survey. In Lighthill, J.,
Sutherland, N. S., Needham, R. M., Longuet-Higgins, H. C., and Michie, D.
(Eds.), Artificial Intelligence: A Paper Symposium. Science Research
Council of Great Britain.
Luger, G. F. and Stubblefield, W. A. 1993. Artificial Intelligence: Structures and
Strategies for Complex Problem Solving (2nd Ed.). Redwood City, CA:
Benjamin/Cummings.
Mackworth, A. K. 1977. Consistency in networks of relations. Artificial Intelligence
8(1): 99–118.
Marr, D. 1982. Vision: A Computational investigation into the human
representation and processing of visual information. W. H. Freeman.
Matuszek, D. L. 2000. A Concise Introduction to LISP
McCarthy, J. 1958. Program with common sense, In proceeding of the Symposium
of Mechanisation of Thought Processes, London, 1: 77-84
165M. S. Okundamiya
McCarthy, J. 1959. Programs with Common Sense, Mechanisation of Thought
Processes, Proceedings of the Symposium of the National Physics
Laboratory, London, U.K., 77-84
McCarthy, J. 1989. Artificial intelligence, logic and formalizing common Sense,
Philosophical Logic and Artificial Intelligence, Kluver.
McCarthy, J. 1990. Formalizing common Sense, Ablex Publishing Corporation.
McCarthy, J. 1996. Concepts of logical artificial intelligence, Tom Mitchell.
McCarthy, J. and P. J. Hayes, 1969. Some philosophical problems from the
standpoint of artificial intelligence, In Meltzer, B., Michie, D., and Swann,
M. (Eds.), Machine Intelligence, Edinburgh University Press, 4: 463–502.
McCulloch, W. S. and W. Pitts, 1943. A logical calculus of the ideas immanent in
nervous activity. Bulletin of Mathematical Biophysics, 5: 115–137.
McDermott, D. 1991. A general framework for reason maintenance. Artificial
Intelligence 50:289–329.
McGregor, J. 1979. Relational consistency algorithms and their applications in
finding subgraph and graph isomorphism. Information Science 19:229–250.
Minsky, M. L. 1975. A framework for representing knowledge, InWinston, P. H.
(Ed.), The Psychology of Computer Vision, pp. 211– 277, McGraw-Hill.
Originally an MIT AI Laboratory memo; the 1975 version is abridged, but is
the most widely cited.
Minsley, M. L. and S. Papert, 1969. Perceptions: An introduction to computational
geometry (1st Ed.) MIT Press, Cambridge.
166 Modern Concepts in Artificial
Minton, S., A. Philips, M. D. Johnston, and P. Laird, 1993. Minimizing conflicts: A
heuristic repair method for constraint-satisfaction and scheduling problems,
Journal of Artificial Intelligence Research, (58): 161–205.
Minton, S., M. Johnston, A. Phillips and P. Laird, 1990. Solving large-scale
constraint-satisfaction and scheduling problems using a heuristic repair
method. In proceedings of the Eighth National Conference on Artificial
Intelligence, Menlo Part, California, 17–24
Nadel, B. 1983. Consistent-labeling problems and their algorithms: Expected
complexities and theory-based heuristics. Artificial Intelligence 21:135–178.
Nadel, B. 1990. Some applications of the constraint-satisfaction problem, Technical
Report, CSC-90-008, Computer Science Dept., Wayne State Univ.
Navinchandra, D. 1990. Exploration and innovationin design. New York: Springer-
Verlag.
Newell, A. and A. A. Simon, 1961. GPS, a program that simulates human thought.
In billing H. (Ed.), Lernende Automaten, Oldenburg, Munich pp. 109-124
Nilson, N. J. 1980. Principles of artificial intelligence, AI Morgan Kaufman San
Mateo, California.
Nilson, N. J. 1991. Logic and artificial intelligence. AI 47 (1-3) : 31-56
Pereira, L. M. and A. Porto, 1982. Selective backtracking. In Logic Programming,
eds. K. Clark and Sten-Ake Tarnlund, 107–114. Boston: Academic.
Petrie, C. 1987. Revised Dependency-Directed Backtracking for Default Reasoning.
In proceedings of the Sixth National Conference on Artificial Intelligence,
167–172, Menlo Park, California.
167M. S. Okundamiya
Petrie, C., R. Causey, D. Steiner and V. Dhar, 1989. A Planning Problem: Revisable
Academic Course Scheduling, Technical Report AI-020-89, MCC Corp.,
Austin, Texas.
Purdom, P. 1983. Search rearrangement backtracking and polynomial average time.
Artificial Intelligence 21:117–133.
Purdom, P., and Brown, C. 1981. An average time analysis of backtracking. SIAM
Journal of Computing 10(3): 583–593.
Purdom, P., and Brown, C. 1982. An empirical comparison of backtracking
algorithms. IEEE Transactions on Pattern Analysis and Machine
Intelligence on Pattern Analysis and Machine Intelligence PAMI-4: 309–
316.
Purdom, P.; Brown, C.; and Robertson, E. L. 1981. Backtracking with Multi-Level
Dynamic Search Rearrangement. Acta Informatica 15:99–113.
Reiter, R. 1980. Logic for default reasoning: A1 B (1-2): 81-132
Rich, E. and Knight, K. (1991). Artificial Intelligence (second edition). New York:
McGraw-Hill.
Rosenblatt, F. 1962. Principles of Neurodynamics: Perceptrons and the Theory of
Brain Mechanisms. Spartan.
Rosiers, W., and Bruynooghe, M. 1986. Empirical Study of Some Constraint-
Satisfaction Problems. In Proceedings of AIMSA 86. New York: Elsevier.
Rumelhart, D. E. and McClelland, J. L. (Eds.). 1986. Parallel Distributed
Processing. MIT Press.
Russell, S. and P. Norvig, 2000. Artificial Intelligence: A modern approach, 2nd Ed.,
Upper Saddle River, New Jersey: Prentice Hall.
168 Modern Concepts in Artificial
Sadeh, N. 1991. Look-Ahead Techniques for Micro-Opportunistic Job Shop
Scheduling. Ph.D. diss., CMU-CS-91-102, Computer Science Dept.,
Carnegie Mellon Univ.
Samuel, A. L. 1959. Some studies in machine learning using the game of checkers.
IBM Journal of Research and Development, 3(3), 210–229.
Schalkoff, R. J. 1990. Artificial Intelligence: An Engineering Approach. New York:
McGraw-Hill.
Shannon, C. E. 1950. Programming a computer for playing chess. Philosophical
Magazine, 41(4), 256–275.
Shapiro, S. C. (Ed.) 1982. Encyclopedia of artificial intelligence, 2nd Ed., Wiley NY.
Shoham, Y. 1983. Agent oriented programming. Artificial Intelligent, 60 (1): 51-92
Simon, H. A. 1957. Models of Man: Social and Rational, John Wiley.
Slagle, J. R. 1963. A heuristic program that solves symbolic integration problems in
freshman calculus. JACM, 10(4).
Solomonoff, R. J. 2009. Algorithmic probability– theory and applications. In
Emmert-Streib, F. and Dehmer, M. (Eds.), Information Theory and Statitical
Learning. Springer.
Stallman, R. and G. J. Sussman, 1977. Forward Reasoning and Dependency-
Directed Backtracking. Artificial Intelligence 9(2): 135–196.
Stan Franklin and Art Graesser 1996. Is it an Agent, or just a Program? : A
Taxonomy for Autonomous Agents; In proceedings of the Third
International Workshop on Agent Theories, Architectures, and Languages,
Springer-Verlag.
169M. S. Okundamiya
Stone, H. S. and J. Stone, 1986. Efficient search techniques: An empirical study of
the n-queens problem, Technical Report RC 12057, IBM T. J. Watson
Research Center, Yorktown Heights, New York.
Tsang, E. P. K. 1987. The consistent labelling problem in temporal reasoning. In
proceedings of the Sixth National Conference on Artificial Intelligence, 251–
255, Menlo Park, California.
Turing, A., Strachey, C., Bates, M. A., and B. V. Bowden, 1953. Digital computers
applied to games. In Bowden, B. V. (Ed.), Faster than Thought, Pitman,
286–310.
Turing, A.M. 1950. Computing machinery and intelligence. Mind, 59: 433-460.
Vilain, M. and H. Kautz, 1986. Constraint-propagation algorithms for temporal
reasoning. In proceedings of the Fifth National Conference on Artificial
Intelligence, Menlo Park, California, 377–382.
Winograd, S. and J. D. Cowan, 1963. Reliable computation in the presence of noise.
MIT Press.
Winston, P. H. 1992. Artificial Intelligence, 3rd Ed., Addison-Wesley.
Zabih, R. and D. McAllester, 1988. A rearrangement Search Strategy for
Determining Propositional Satisfiability. In proceedings of the Seventh
National Conference on Artificial Intelligence, Menlo Park, California, 155–
160.