NDLI: Interactively learned probabilistic context-sensitive grammar in genetic programming for the evolution of snake-like robot

Content Provider	IEEE Xplore Digital Library
Author	Tanev, I. Shimohara, K.
Copyright Year	2009
Description	Author affiliation: Department of Information System Design, Doshisha University, Kyoto, Japan (Tanev, I.; Shimohara, K.)
Abstract	We discuss an approach of incorporating interactively learned consensus sequences (ILCS) in genetic programming (GP) for efficient evolution of simulated Snakebot situated in a challenging environment. ILCS introduce a biased mutation in GP via probabilistic context sensitive grammar, in which the probabilities of applying the production rules with multiple right-hand side alternatives depend on the grammatical context. The distribution of these probabilities is learned interactively from the syntax of the Snakebots, exhibiting behavioral traits that according to the human observer are relevant for the emergence of ability to overcome obstacles. Because at the earlier stages of evolution these behavioral traits are not necessarily pertinent to the best performing (i.e. fastest) Snakebots, the user feedback provides the evolution with an additional insight about the promising areas in the fitness landscape. Empirical results verify that employing ILCS improves the efficiency of GP in that the evolved Snakebots are faster than those obtained via canonical GP.
Starting Page	2732
Ending Page	2737
File Size	495409
Page Count	6
File Format	PDF
ISBN	9784907764340
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-18
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Genetic programming Mobile robots Automatic control Information systems Electronic mail Legged locomotion Vehicles Context modeling Genetic mutations Production Consensus Sequence Interactive Evolution Locomotion Snake-like Robot Context-Sensitive Grammar
Content Type	Text
Resource Type	Article

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