Implications of Incorporating Learning Probabilistic Context-sensitive Grammar in Genetic Programming on Evolvability of Adaptive Locomotion Gaits of Snakebot
نویسنده
چکیده
In this work we propose an approach of incorporating learning contextsensitive grammar in strongly typed genetic programming (GP) employed for evolution and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach the probabilistic context-sensitive grammar is derived from the originally defined context-free grammar (which usually expresses the syntax of genetic programs in strongly typed GP), using aggregated reward values obtained from the evolved best-of-run healthy, undamaged Snakebots. The probabilities of applying each of particular production rules with multiple right-hand side alternatives in derived probabilistic context-sensitive grammar depend on the context, and these probabilities are “learned” from the aggregated reward values. Empirically obtained results indicate that employing probabilistic context-sensitive grammar contributes to the improving the ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics. Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. In all considered cases of inflicted partial damage of 1, 2, 4, and 8 out of 15 morphological segments, the incorporation of learning context sensitive grammar in GP improves the evolvability of adaptive locomotion gaits in that the recovery of partially damaged Snakebot is (i) faster and to (ii) higher values of velocity of locomotion.
منابع مشابه
International Journal of Mathematics and Computer Sciences (IJMCS) ISSN: 2305-7661 Vol.21 September 2013 International Scientific Researchers (ISR)
In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained...
متن کاملGenetic transposition inspired incremental genetic programming for efficient coevolution of locomotion and sensing of simulated snake-like robot
Genetic transposition (GT) is a process of moving sequences of DNA to different positions within the genome of a single cell. It is recognized that the transposons (the jumping genes) facilitate the evolution of increasingly complex forms of life by providing the creative playground for the mutation where the latter could experiment with developing novel genetic structures without the risk of d...
متن کاملOn the Analogy in the Emergent Properties of Evolved Locomotion Gaits of Simulated Snakebot
متن کامل
Evolution, Robustness, and Adaptation of Sidewinding Locomotion of Simulated Snake-Like Robot
Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming, of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all phy...
متن کاملStudying impressive parameters on the performance of Persian probabilistic context free grammar parser
In linguistics, a tree bank is a parsed text corpus that annotates syntactic or semantic sentence structure. The exploitation of tree bank data has been important ever since the first large-scale tree bank, The Penn Treebank, was published. However, although originating in computational linguistics, the value of tree bank is becoming more widely appreciated in linguistics research as a whole. F...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004