Analogical and inductive inference : International Workshop AII '92, Dagstuhl Castle, Germany, October 5-9, 1992 : proceedings
Author(s)
Bibliographic Information
Analogical and inductive inference : International Workshop AII '92, Dagstuhl Castle, Germany, October 5-9, 1992 : proceedings
（Lecture notes in computer science, 642 . Lecture notes in artificial intelligence）
Springer-Verlag, c1992
- : us
- : gw
Available at / 53 libraries
-
Library, Research Institute for Mathematical Sciences, Kyoto University数研
: usL/N||LNCS||64292043742
-
University of Tsukuba Library, Library on Library and Information Science
: gw007.08:L-49:642921006770
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references
Description and Table of Contents
Description
This volume contains the text of the five invited papers and
16 selected contributions presented at the third
International Workshop on Analogical and Inductive
Inference, AII `92, held in Dagstuhl Castle, Germany,
October 5-9, 1992.
Like the two previous events, AII '92 was intended to bring
together representatives from several research communities,
in particular, from theoretical computer science, artificial
intelligence, and from cognitive sciences.
The papers contained in this volume constitute a
state-of-the-art report on formal approaches to algorithmic
learning, particularly emphasizing aspects of analogical
reasoning and inductive inference. Both these areas are
currently attracting strong interest: analogical reasoning
plays a crucial role in the booming field of case-based
reasoning, and, in the fieldof inductive logic programming,
there have recently been developed a number of new
techniques for inductive inference.
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.
by "Nielsen BookData"
