Seeing the light: artificial evolution, real vision
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
New Robotics: Design Principles for Intelligent Systems
Artificial Life
Toward a Theory of Embodied Statistical Learning
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Higher Coordination With Less Control-A Result of Information Maximization in the Sensorimotor Loop
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
On-line, on-board evolution of robot controllers
EA'09 Proceedings of the 9th international conference on Artificial evolution
R-IAC: Robust Intrinsically Motivated Exploration and Active Learning
IEEE Transactions on Autonomous Mental Development
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This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach.