Machine Learning, Meta-Reasoning and Logics

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.

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I. Meta-Reasoning and Machine Learning.- A Metalevel Manifesto.- A Sketch of Autonomous Learning using Declarative Bias.- Shift of Bias as Non-Monotonic Reasoning.- Mutual Constraints on Representation and Inference.- Meta-Reasoning: Transcription of Invited Lecture by Luigia Aiello.- II. Reasoning About Proofs and Explanations.- Overgenerality in Explanation-Based Generalization.- A Tool for the Management of Incomplete Theories: Reasoning about Explanations.- A Comparison of Rule and Exemplar-Based Learning Systems.- Discovery and Revision via Incremental Hill Climbing.- Learning from Imperfect Data.- III. Foundations of AI and Machine Learning.- Knowledge Revision and Multiple Extensions.- Minimal Change-A Criterion for Choosing between Competing Models.- Hierarchic Autoepistemic Theories for Nonmonotonic Reasoning: Preliminary Report.- Automated Quantified Modal Logic.