Evolution of Neural Architecture Fitting Environmental Dynamics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
How to promote generalisation in evolutionary robotics: the ProGAb approach
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
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How to drive a learning process towards the emergence of a memory? It is hypothesized here that a reward function which evaluates the fulfillment of a task requiring memory does not necessarily reward the stepping stones to this cognitive ability. This question is studied from an evolutionary robotics perspective. Both structure and parameters of a neural network supposed to exhibit a memory are generated through an evolutionary search. Results show that selective pressures driving the evolutionary search are of critical importance. We further hypothesize that one feature of controllers with a memory is their ability to exhibit consistent behaviors over different contexts. To validate this hypothesis, a new fitness objective rewarding behavior consistency in different contexts is introduced and tested on a T-maze ER task --- a task involving both navigation and working memory. The efficiency of the fitness objective is studied, as well as its effects on the overall performance and generalization ability of the controller. Results show that it is complementary to a behavioral diversity objective, thus leading to improved results when using both selection pressures.