Overview
- Editors:
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Pavel B. Brazdil
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University of Porto, Portugal
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Kurt Konolige
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SRI International, USA
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Table of contents (14 chapters)
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Meta-Reasoning and Machine Learning
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- Stuart J. Russell, Benjamin N. Grosof
Pages 19-53
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- Benjamin N. Grosof, Stuart J. Russell
Pages 55-83
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- Stuart Russell, Devika Subramanian
Pages 85-106
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Back Matter
Pages 113-118
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Reasoning about Proofs and Explanations
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Front Matter
Pages 119-119
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- Béatrice Duval, Yves Kodratoff
Pages 135-158
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- Pavel Brazdil, Peter Clark
Pages 207-232
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Foundations of AI and Machine Learning
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Front Matter
Pages 233-233
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- L. Fariñas del Cerro, A. Herzig
Pages 301-317
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Back Matter
Pages 319-328
About this book
This book contains a selection of papers presented at the International Workshop Machine Learning, Meta-Reasoning and Logics held in Hotel de Mar in Sesimbra, Portugal, 15-17 February 1988. All the papers were edited afterwards. The Workshop encompassed several fields of Artificial Intelligence: Machine Learning, Belief Revision, Meta-Reasoning and Logics. The objective of this Workshop was not only to address the common issues in these areas, but also to examine how to elaborate cognitive architectures for systems capable of learning from experience, revising their beliefs and reasoning about what they know. Acknowledgements The editing of this book has been supported by COST-13 Project Machine Learning and Knowledge Acquisition funded by the Commission o/the European Communities which has covered a substantial part of the costs. Other sponsors who have supported this work were Junta Nacional de lnvestiga~ao Cientlfica (JNICT), lnstituto Nacional de lnvestiga~ao Cientlfica (INIC), Funda~ao Calouste Gulbenkian. I wish to express my gratitude to all these institutions. Finally my special thanks to Paula Pereira and AnaN ogueira for their help in preparing this volume. This work included retyping all the texts and preparing the camera-ready copy. Introduction 1 1. Meta-Reasoning and Machine Learning The first chapter is concerned with the role meta-reasoning plays in intelligent systems capable of learning. As we can see from the papers that appear in this chapter, there are basically two different schools of thought.
Editors and Affiliations
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University of Porto, Portugal
Pavel B. Brazdil
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SRI International, USA
Kurt Konolige
