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Artificial Intelligence Through Prolog - Neil C Rowe.pdf
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Artificial Intelligence Through Prolog - Neil C Rowe.pdf Artificial Intelligence Through Prolog - Neil C Rowe.pdf
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Artificial Intelligence through Prolog by Neil C. Rowe
Artificial Intelligence through Prolog by Neil C.
Rowe
Prentice-Hall, 1988, ISBN 0-13-048679-5
Full text of book (without figures)
Table of Contents
Preface
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
Some figures in crude form
Instructor's Manual, containing additional answers and exercises
Errata on the book as published
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Table of contents
Preface
Acknowledgements
To the reader
1. Introduction
1.1 What artificial intelligence is about
1.2 Understanding artificial intelligence
1.3 Preview
2. Representing facts
2.1 Predicates and predicate expressions
2.2 Predicates indicating types
2.3 About types
2.4 Good naming
2.5 Property predicates
2.6 Predicates for relationships
2.7 Semantic networks
2.8 Getting facts from English descriptions
2.9 Predicates with three or more arguments
2.10 Probabilities
2.11 How many facts do we need?
3. Variables and queries
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3.1 Querying the facts
3.2 Queries with one variable
3.3 Multi-directional queries
3.4 Matching alternatives
3.5 Multi-condition queries
3.6 Negative predicate expressions
3.7 Some query examples
3.8 Loading a database
3.9 Backtracking
3.10 A harder backtracking example: superbosses
3.11 Backtracking with "not"s
3.12 The generate-and-test scheme
3.13 Backtracking with "or"s (*)
3.14 Implementation of backtracking
3.15 About long examples
4. Definitions and inferences
4.1 Rules for definitions
4.2 Rule and fact order
4.3 Rules as programs
4.4 Rules in natural language
4.5 Rules without right sides
4.6 Postponed binding
4.7 Backtracking with rules
4.8 Transitivity inferences
4.9 Inheritance inferences
4.10 Some implementation problems for transitivity and inheritance
4.11 A longer example: some traffic laws
4.12 Running the traffic lights program
4.13 Declarative programming
5. Arithmetic and lists in Prolog
5.1 Arithmetic comparisons
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5.2 Arithmetic assignment
5.3 Reversing the "is"
5.4 Lists in Prolog
5.5 Defining some list-processing predicates
5.6 List-creating predicates
5.7 Combining list predicates
5.8 Redundancy in definitions
5.9 An example: dejargonizing bureaucratese (*)
6. Control structures for rule-based systems
6.1 Backward-chaining control structures
6.2 Forward chaining
6.3 A forward chaining example
6.4 Hybrid control structures
6.5 Order variants
6.6 Partitioned control structures
6.7 Meta-rules
6.8 Decision lattices
6.9 Concurrency in control structures
6.10 And-or-not lattices
6.11 Randomness in control structures
6.12 Grammars for interpreting languages (*)
7. Implementation of rule-based systems
7.1 Implementing backward chaining
7.2 Implementing virtual facts in caching
7.3 Input coding
7.4 Output coding
7.5 Intermediate predicates
7.6 An example program
7.7 Running the example program
7.8 Partitioned rule-based systems
7.9 Implementing the rule-cycle hybrid
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7.10 Implementing pure forward chaining (*)
7.11 Forward chaining with "not"s (*)
7.12 General iteration with "forall" and "doall" (*)
7.13 Input and output of forward chaining (*)
7.14 Rule form conversions (*)
7.15 Indexing of predicates (*)
7.16 Implementing meta-rules (*)
7.17 Implementing concurrency (*)
7.18 Decision lattices: a compilation of a rule-based system (*)
7.19 Summary of the code described in the chapter (*)
8. Representing uncertainty in rule-based systems
8.1 Probabilities in rules
8.2 Some rules with probabilities
8.3 Combining evidence assuming statistical independence
8.4 Prolog implementation of independence-assumption "and-combination"
8.5 Prolog implementation of independence-assumption "or-combination"
8.6 The conservative approach
8.7 The liberal approach and others
8.8 Negation and probabilities
8.9 An example: fixing televisions
8.10 Graphical representation of probabilities in rule-based systems
8.11 Getting probabilities from statistics
8.12 Probabilities derived from others
8.13 Subjective probabilities
8.14 Maximum-entropy probabilities (*)
8.15 Consistency (*)
9. Search
9.1 Changing worlds
9.2 States
9.3 Three examples
9.4 Operators
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