Incorporating learning probabilistic context-sensitive grammar in genetic programming for efficient evolution and adaptation of snakebot

In this work we propose an approach of incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP) employed for evolution and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach LPCSG is derived from the originally defined context-free grammar, which usually expresses the syntax of genetic programs in canonical GP. During the especially introduced steered mutation the probabilities of applying each of particular production rules with multiple right-hand side alternatives in LPCSG depend on the context, and these probabilities are learned from the aggregated reward values obtained from the evolved best-of-generation Snakebots. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of Snakebot. In all of the cases considered in this study, the locomotion gaits, evolved and adapted employing GP with LPCSG feature higher velocity and are obtained faster than with canonical GP.