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  • Conference proceedings
  • © 1998

Learning and Reasoning with Complex Representations

PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations Cairns, Australia, August 26-30, 1996, Selected Papers

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1359)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Conference series link(s): PRICAI: Pacific Rim International Conference on Artificial Intelligence

Conference proceedings info: PRICAI 1996.

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Table of contents (18 papers)

  1. Front Matter

  2. Inductive constraint logic programming: An overview

    • Srinivas Padmanabhuni, Aditya K. Ghose
    Pages 1-8
  3. Some approaches to reasoning with incomplete and changing information

    • Grigoris Antoniou, Mary-Anne Williams
    Pages 9-44
  4. Curried least general generalization: A framework for higher order concept learning

    • Srinivas Padmanabhuni, Randy Goebel, Koichi Furukawa
    Pages 45-60
  5. Approximate validity

    • Henry E. Kyburg Jr.
    Pages 61-77
  6. Inductive theories from equational systems

    • Michael Bulmer
    Pages 78-91
  7. The role of default representations in incremental learning

    • Aditya K. Ghose, Srinivas Padmanabhuni, Randy Goebel
    Pages 92-105
  8. Learning stable concepts in a changing world

    • Michael Harries, Kim Horn
    Pages 106-122
  9. Inducing complex spatial descriptions in two dimensional scenes

    • Brendan McCane, Terry Caelli, Olivier de Vel
    Pages 123-132
  10. A framework for learning constraints: Preliminary report

    • Srinivas Padmanabhuni, Jia-Huai You, Aditya Ghose
    Pages 133-147
  11. Induction of constraint logic programs

    • Michèle Sebag, Céline Rouveirol, Jean-François Puget
    Pages 148-167
  12. A Group Decision and Negotiation Support System for argumentation based reasoning

    • Nikos Karacapilidis, Dimitris Papadias
    Pages 188-205
  13. Modelling inertia in action languages

    • Mikhail Prokopenko, Pavlos Peppas
    Pages 234-247
  14. Probabilistic diagnosis as an update problem

    • Angelo C. Restificar
    Pages 256-266
  15. Back Matter

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  1. Learning and Reasoning with Complex Representations

About this book

This book constitutes the thoroughly revised and refereed post-workshop documentation of two international workshops held in conjunction with the Pacific Rim International Conference on Artificial Intelligence, PRICAI'96, in Cairns, Australia, in August 1996.
The volume presents 14 revised full papers togehter with two invited contributions and two introductory surveys particularly commissioned for this book. Among the topics addressed are computational learning, commonsense reasoning, constraint logic programming, fuzzy reasoning, vague data, inductive inference, belief revision, action theory, uncertainty, and probabilistic diagnosis.

Bibliographic Information

  • Book Title: Learning and Reasoning with Complex Representations

  • Book Subtitle: PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations Cairns, Australia, August 26-30, 1996, Selected Papers

  • Editors: Grigoris Antoniou, Aditya K. Ghose, Mirosław Truszczyński

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/3-540-64413-X

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1998

  • Softcover ISBN: 978-3-540-64413-2Published: 15 April 1998

  • eBook ISBN: 978-3-540-69780-0Published: 15 November 2006

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XII, 288

  • Topics: Artificial Intelligence

Buy it now

Buying options

Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access