Prolog Programming for Artificial Intelligence / Edition 2by Ivan Bratko
Pub. Date: 06/28/1990
B> This best-selling guide to Prolog has been fully revised and extended to provide an even greater range of applications, enhancing its value as a stand-alone guide to Prolog, artificial intelligence, or AI programming. Ivan Bratko discusses natural language processing with grammar rules, planning, and machine learning. The coverage of meta-programming/u>/b>/i>… See more details below
B> This best-selling guide to Prolog has been fully revised and extended to provide an even greater range of applications, enhancing its value as a stand-alone guide to Prolog, artificial intelligence, or AI programming. Ivan Bratko discusses natural language processing with grammar rules, planning, and machine learning. The coverage of meta-programming includes meta-interpreters and object-oriented programming in Prolog. The new edition includes coverage of: constraint logic programming; qualitative reasoning; inductive logic programming; recently developed algorithms; belief networks for handling uncertainty; and a major update on machine learning. This book is aimed at programmers who need to learn AI programming.
Table of Contents
I. THE PROLOG LANGUAGE.
Defining relations by rules.
How Prolog answers questions.
Declarative and procedural meaning of programs.
2. Syntax and Meaning of Prolog Programs.
Declarative meaning of Prolog programs.
Example: monkey and banana.
Order of clauses and goals.
The relation between Prolog and logic.
3. Lists, Operators, Arithmetic.
Some operations on lists.
4. Using Structures: Example Programs.
Doing data abstraction.
Simulating a non-deterministic automaton.
The right queens problem.
5. Controlling Backtracking.
Examples using cut.
Negation as failure.
Problems with cut and negation.
6. Input and Output.
Processing files of terms.
Constructing and decomposing atoms.
7. More Built-In Predicates.
Constructing and decomposing terms: =..,functor, arg, name.
Various kinds of equality and comparison.
bagof, setof, and findall.
8. Programming Style and Technique.
How to think about Prolog programs.
9. Operations on Data Structures.
Representing sets by binary trees.
Insertion and deletion in a binary dictionary.
10. Advanced Tree Representations.
AVL-tree: an approximately balanced tree.
II. PROLOG IN ARTIFICIAL INTELLIGENCE.
Depth-first search and iterative deepening.
Analysis of basic search techniques.
12. Best-First Heuristic Search.
Best-first search applied to the eight puzzle.
Best-first search applied to scheduling.
Space-saving techniques for best-first search.
13. Problem Decomposition and AND/OR Graphs.
Examples of AND/OR search representation.
Basic AND/OR search procedures.
Best-first AND/OR search.
14. Constraint Logic Programming
CLP over real numbers: CLP(R)
Scheduling with CLP.
A simulation program with constraints.
CLP over finite domains: CLP (FD).
15. Knowledge Representation and Expert Systems.
Representing knowledge with if-then rules.
Forward and backward chaining in rule-based systems.
Semantic networks and frames.
16. An Expert System Shell.
Designing the inference engine.
Deriving plans by means-ends analysis.
Procedural aspects and breadth-first regime.
Combining means-ends planning with best-first heuristic.
Uninstantiated actions and partial-order planning.
18. Machine Learning.
The problem of learning concepts from examples.
Learning relational descriptions: a detailed example.
Learning simple if-then rules.
Induction of decision trees.
Learning from noisy data and tree pruning.
Success of learning.
19. Inductive Logic Programming.
Constructing Prolog programs from examples.
20. Qualitative Reasoning.
Qualitative reasoning about static systems.
Qualitative reasoning about dynamic systems.
A qualitative simulation program.
Discussion of the qualitative simulation program.
21. Language Processing with Grammar Rules.
Defining the meaning of natural language.
22. Game Playing.
The minimax principle.
The alpha-beta algorithm: an efficient implementation of minimax.
Minimax-based programs: refinements and limitations.
Pattern knowledge and the mechanism of 'advice'.
A chess endgame program in Advice Language O
A simple theorem prover as a pattern-directed program.
Appendix A: Some Differences Between Prolog Implementations.
Appendix B. Some Frequently Used Predicates.
Solutions to Selected Exercises.
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