The synthesis of digital machines with provable epistemic properties
Proceedings of the 1986 Conference on Theoretical aspects of reasoning about knowledge
Explorations in evolutionary robotics
Adaptive Behavior
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
A situated view of representation and control
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Evolving non-Trivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
Evolving mobile robots in simulated and real environments
Artificial Life
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Two-Step Incremental Evolution of a Prosthetic Hand Controller Based on Digital Logic Gates
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Possibilities and Limitations of Applying Evolvable Hardware to Real-World Applications
FPL '00 Proceedings of the The Roadmap to Reconfigurable Computing, 10th International Workshop on Field-Programmable Logic and Applications
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Building robots can be a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to cope the difficulties in designing robots is to adopt learning methods. Evolution-based approaches are a special kind of machine learning method and during the last few years some researchers have shown the advantages of using this kind of approach to automate the design of robots. However, the tasks achieved so far are fairly simple. In this work, we analyse the difficulties of applying evolutionary approaches to learn complex behaviours for mobile robots. And, instead of evolving the controller as a whole, we propose to take the control architecture of a behavior-based system and to learn the separate behaviours and the arbitration by the use of an evolutionary approach. By using the technique of task decomposition, the job of defining fitness functions becomes more straightforward and the tasks become easier to achieve. To assess the performance of the developed approach, we have evolved a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach.