Overview

This volume contains the text of the five invited papers and16 selected contributions presented at the thirdInternational Workshop on Analogical and InductiveInference, AII 92, held in Dagstuhl Castle, Germany,October 5-9, 1992. Like the two previous events, AII '92 was intended to bringtogether representatives from several research communities,in particular, from theoretical computer science, artificialintelligence, and from cognitive sciences. The papers contained in this volume constitute astate-of-the-art report on formal approaches to algorithmiclearning, particularly emphasizing aspects of analogicalreasoning and inductive inference. Both these areas arecurrently attracting strong interest: analogical reasoningplays a crucial role in the booming field of case-basedreasoning, and, in the fieldof inductive logic programming,there have recently been developed a number of newtechniques for inductive inference.

Full Product Details

Author:   Klaus P. Jantke
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   1992 ed.
Volume:   642
Dimensions:   Width: 15.50cm , Height: 1.70cm , Length: 23.50cm
Weight:   1.040kg
ISBN:  

9783540560043

ISBN 10:   3540560041
Pages:   326
Publication Date:   23 September 1992
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Representing the spatial/kinematic domain and lattice computers.- A solution of the credit assignment problem in the case of learning rectangles.- Learning decision strategies with genetic algorithms.- Background knowledge and declarative bias in inductive concept learning.- Too much information can be too much for learning efficiently.- Some experiments with a learning procedure.- Unions of identifiable classes of total recursive functions.- Learning from multiple sources of inaccurate data.- Strong separation of learning classes.- Desiderata for generalization-to-N algorithms.- The power of probabilism in Popperian FINite learning.- An analysis of various forms of ‘jumping to conclusions’.- An inductive inference approach to classification.- Asking questions versus verifiability.- Predictive analogy and cognition.- Learning a class of regular expressions via restricted subset queries.- A unifying approach to monotonic language learning on informant.- Characterization of finite identification.- A model of the ‘redescription’ process in the context of geometric proportional analogy problems.- Inductive strengthening: The effects of a simple heuristic for restricting hypothesis space search.- On identifying DNA splicing systems from examples.

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