Prolog Programming for Artificial Intelligence, 4th edition

Published by Addison-Wesley (24 August 2011) © 2012

  • Ivan Bratko University of Ljubljana
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Title overview

Understanding artificial intelligence requires both strong programming skills and practical experience applying AI techniques to real problems. Programming in Prolog for Artificial Intelligence combines an introduction to the Prolog programming language with a comprehensive exploration of AI methods and their implementation. The content takes a hands-on approach, focusing on how Prolog can be used to solve challenging problems rather than simply presenting language features. By linking programming concepts with AI applications, learners gain a clear understanding of how intelligent systems are designed, represented, and implemented in practice.

Learners develop practical skills in Prolog programming while building a broad understanding of core artificial intelligence techniques. Through topics including search, planning, knowledge representation, expert systems, machine learning, probabilistic reasoning, natural language processing, and game playing, learners gain experience implementing AI solutions and evaluating different approaches. The emphasis on practical problem solving helps learners understand how AI methods can be applied to real-world challenges. Expanded coverage of constraint logic programming, planning, and reasoning techniques provides valuable insight into both established and evolving areas of artificial intelligence.

Table of contents

  • Part i  The Prolog Language   
  •   1   Introduction to Prolog   
  •   2   Syntax and Meaning of Prolog Programs
  •   3   Lists, Operators, Arithmetic   
  •   4   Programming Examples   
  •   5   Controlling Backtracking   
  •   6   Built-in Predicates   
  •   7   Constraint Logic Programming
  •   8   Programming Style and Technique   
  •   9   Operations on Data Structures   
  • 10   Balanced Trees   
  • Part ii  Prolog in Artificial Intelligence   
  • 11   Problem-Solving as Search
  • 12   Heuristic Search and A* Algorithm
  • 13   Best-First Search: Minimising Time and Space
  • 14   Problem Decomposition and AND/OR Graphs
  • 15   Knowledge Representation and Expert Systems   
  • 16   Probabilistic Reasoning with Bayesian Networks
  • 17   Planning   
  • 18   Partial order planning and GRAPHPLAN
  • 19   Scheduling, Simulation and Control with CLP
  • 20   Machine Learning   
  • 21   Inductive Logic Programming   
  • 22   Qualitative Reasoning   
  • 23    Language Processing with Grammar Rules
  • 24    Game Playing
  • 25   Meta-Programming   
  • Appendix A: Some Differences Between Prolog Implementations   
  • Appendix B: Some Frequently Used Predicates   
  • Solutions to Selected Exercises   
  • Index 

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