Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who k
Machine Learning, Meta-Reasoning and Logics
โ Scribed by D. Paul Benjamin (auth.), Pavel B. Brazdil, Kurt Konolige (eds.)
- Publisher
- Springer US
- Year
- 1989
- Tongue
- English
- Leaves
- 338
- Series
- The Kluwer International Series in Engineering and Computer Science 82
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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.
โฆ Table of Contents
Front Matter....Pages i-xx
Front Matter....Pages 1-1
A Metalevel Manifesto....Pages 3-17
A Sketch of Autonomous Learning using Declarative Bias....Pages 19-53
Shift of Bias as Non-Monotonic Reasoning....Pages 55-83
Mutual Constraints on Representation and Inference....Pages 85-106
Meta-Reasoning: Transcription of an Invited Lecture by....Pages 107-112
Back Matter....Pages 113-118
Front Matter....Pages 119-119
Overgenerality in Explanation-Based Generalization....Pages 121-134
A Tool for the Management of Incomplete Theories: Reasoning about Explanation....Pages 135-158
A Comparison of Rule and Exemplar-Based Learning Systems....Pages 159-186
Discovery and Revision via Incremental Hill Climbing....Pages 187-206
Learning from Imperfect Data....Pages 207-232
Front Matter....Pages 233-233
Knowledge Revision and Multiple Extensions....Pages 235-255
Minimal Change โ A Criterion for Choosing between Competing Models....Pages 257-276
Hierarchic Autoepistemic Theories for Nonmonotonic Reasoning: Preliminary Report....Pages 277-299
Automated Quantified Modal Logic....Pages 301-317
Back Matter....Pages 319-328
โฆ Subjects
Artificial Intelligence (incl. Robotics)
๐ SIMILAR VOLUMES
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who k
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who k