Advances in Artificial Intelligence

1. Front Matter

   Pages i-xii

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2. Learning from Experience in Board Games

   • Ze’ev Ben-Porat, Martin Charles Golumbic

   Pages 1-25

3. PRODS: A Prototype Based Design Shell for Prototype Selection and Prototype Refinement

   • Rivka E. Oxman

   Pages 26-42

4. What’s in a Joke?

   • Michal Ephratt

   Pages 43-74

5. Machinery for Hebrew Word Formation

   • Uzzi Ornan

   Pages 75-93

6. Theory Formation for Interpreting an Unknown Language

   • Ephraim Nissan

   Pages 95-113

7. Ontology, Sublanguage, and Semantic Networks in Natural Language Processing

   • Victor Raskin

   Pages 114-128

8. An Incremental Conceptual Clustering Algorithm that Reduces Input-Ordering Bias

   • Yoëlle S. Maarek

   Pages 129-144

9. Anticipating a Listener’s Response in Text Planning

   • Ingrid Zukerman

   Pages 145-170

10. Towards an Intelligent Finite Element Training System

    • Alex Bykat

    Pages 171-181

11. Bayesian Inference in an Expert System without Assuming Independence

    • Alex Gammerman, A. R. Thatcher

    Pages 182-218

12. A Partial Orders Semantics for Constraint Based Systems

    • Steven A. Battle

    Pages 219-226

13. Partial Orders as a Basis for KBS Semantics

    • Simon P. H. Morgan, John G. Gammack

    Pages 227-246

14. A Heuristic Search Approach to Planning and Scheduling Software Manufacturing Projects

    • Ali Safavi, Stephen F. Smith

    Pages 247-268

15. From Data to Knowledge Bases

    • Martin Charles Golumbic, Dennis Grinberg

    Pages 269-294

16. Back Matter

    Pages 295-303

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Research in artificial intelligence, natural language processing and knowledge-based systems has blossomed during the past decade. At national and international symposia as well as in research centers and universities all over the world, these subjects have been the focus of intense debate and study. This is equally true in Israel which has hosted several international forums on these topics. The articles in this book represent a selection of contributions presented at recent AI conferences held in Israel. A theoretical model for a system that learns from its own experience in playing board games is presented in Learning from Experience in Board Games by Ze'ev Ben-Porat and Martin Golumbic. The model enables such a system to enhance and improve its playing capabilities through the use of a learning mechanism which extracts knowledge from actual playing experience. The learning process requires no external guidance or assistance. This model was implemented and tested on a variant of "Chinese Checkers. " The paper shows the feasibility and validity of the proposed model and investigates the parameters that affect its performance traits. The experimental results give evidence of the validity of the model as a powerful learning mechanism. Original and general algorithms for knowledge extraction and pattern matching were designed and tested as part of the prototype computer system. Analysis of the performance characteristics of these algorithms indicates that they can handle large knowledge bases in an efficient manner.

• Pattern Matching
• artificial intelligence
• expert system
• intelligence
• knowledge
• knowledge base
• knowledge-based system
• knowledge-based systems
• learning
• natural language
• natural language processing
• ontology

• IBM T.J. Watson Research Center, Yorktown Heights, USA

  Martin Charles Golumbic

• IBM Israel Scientific Center, Technion City, Haifa, Israel

  Martin Charles Golumbic

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