Reducing trials by thinning-out in skill discovery

Hayato Kobayashi, Kohei Hatano, Akira Ishino, Ayumi Shinohara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this paper, we propose a new concept, thinning-out, for reducing the number of trials in skill discovery. Thinning-out means to skip over such trials that are unlikely to improve discovering results, in the same way as "pruning" in a search tree. We show that our thinning-out technique significantly reduces the number of trials. In addition, we apply thinning-out to the discovery of good physical motions by legged robots in a simulation environment. By using thinning-out, our virtual robots can discover sophisticated motions that is much different from the initial motion in a reasonable amount of trials.

Original languageEnglish
Title of host publicationDiscovery Science - 10th International Conference, DS 2007, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540754879
Publication statusPublished - 2007
Event10th International Conference on Discovery Science, DS 2007 - Sendai, Japan
Duration: Oct 1 2007Oct 4 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4755 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Conference on Discovery Science, DS 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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