Analogical and Inductive Inference

International Workshop AII'86 Wendisch-Rietz, GDR, October 6-10, 1986, Proceedings
Herausgegeben:Jantke, Klaus P.

• Broschiertes Buch

Produktbeschreibung

This volume contains revised versions of presentations at the International Workshop on Analogical and Inductive Inference (AII '86) held in Wendisch-Rietz, GDR, October 16-10, 1986. Inductive inference and analogical reasoning are two basic approaches to learning algorithms. Both allow for exciting problems and promising concepts of invoking deeper mathematical results for considerable advances in intelligent software systems. Hence analogical and inductive inference may be understood as a firm mathematical basis for a large variety of problems in artificial intelligence. While the papers on inductive inference contain technical results and reflect the state of the art of this well-developed mathematical theory, those devoted to analogical reasoning reflect the ongoing process of developing the basic concepts of the approach. The workshop thus contributes significantly to the advancement of this field.

Produktdetails

• Lecture Notes in Computer Science 265
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-18081-4
• 1987.
• Seitenzahl: 240
• Erscheinungstermin: 15. Juli 1987
• Englisch
• Abmessung: 235mm x 155mm x 14mm
• Gewicht: 345g
• ISBN-13: 9783540180814
• ISBN-10: 3540180818
• Artikelnr.: 23144264

Inhaltsangabe

Towards the development of an analysis of learning algorithms.- Using the algorithm of analogy for generation of robot programs.- On the inference of sequences of functions.- Fixed point equations as hypotheses in inductive reasoning.- Inductive inference of functions from noised observations.- Reasoning by analogy as a partial identity between models.- Can missing information be also useful?.- A decidability problem of church-rosser specifications for program synthesis.- Some considerations about formalization of analogical reasoning.- Analogical reasoning using graph transformations.- Knowledge acquisition by inductive learning from examples.- On the inference of programs approximately computing the desired function.- Stratified inductive hypothesis generation.- A model theoretic oriented approach to analogy.- On the complexity of effective program synthesis.- On barzdin's conjecture.