Summary
In this paper we discuss some of the new work we have been carrying out with the objective of making evolutionarily obtained behavior based architectures and modules for autonomous robots more standardized and interchangeable. The architectures contemplated here are based on a multiple behavior structure where all of the modules, as well as their interconnections, are automatically obtained through evolutionary processes. The main objective of this line of research is to obtain procedures that permit producing behavior based controllers that work on real robots operating in real environments as independently of the platform as possible. In this particular paper we will concentrate on different aspects regarding the inclusion of virtual sensors as a way to make improved use of the capabilities of the different platforms and on the reuse of behavior modules. This reuse will be contemplated within the same behavioral architecture and from the point of view of transferring behavior modules from one platform to a different one.
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References
Arkin, R. C. (1998) Behavior Based Robotics, MIT Press, Cambridge, MA.
Becerra, J. A.; Santos, J.; and Duro, R. J. (1999) Progressive Construction of Compound Behavior Controllers for Autonomous Robots Using Temporal Information, Advances in Artificial Life, Dario Floreano, Jean-Daniel Nicoud and Francesco Mondada (Eds.), Lecture Notes in Artificial Intelligence, Vol. 1674, pp. 324–328, Springer-Verlag, Berlin.
Beer, R. D. and Gallagher, J. C. (1992) Evolving Dynamical Neural Networks for Adaptive Behavior, Adaptive Behavior, Vol. 1, No. 1, pp. 91–122.
Brooks, R. A.; (1991) Intelligence without Representation, Artificial Intelligence, Vol. 47, 139–159.
Cliff, D.; Harvey, I. and Husbands, P. (1992) Incremental Evolution of Neural Network Architectures for Adaptive Behaviour, Tech. Rep. No. CSRP256, Brighton, School of Cognitive and Computing Sciences, University of Sussex, UK.
Floreano, D. and Mondada, F. (1998) Evolutionary Neurocontrollers for Autonomous Mobile Robots, Neural Networks, Vol. 11, pp. 1461–1478.
Harvey, I.; Husbands, P. and Cliff, D. (1993) Issues in Evolutionary Robotics, J.-A. Meyer, H. Roitblat, and S. Wilson (eds.), From Animals to Animats 2. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92), MIT Press, Cambridge, MA, pp. 364–373.
Jakobi, N. (1997) Evolutionary Robotics and the Radical Envelope of Noise Hypothesis, Adaptive Behavior, Vol. 6, No. 2, pp. 325–368.
Maes, P. A. (1990) Bottom-up Mechanism for Behavior Selection in an Artificial Creature, Proceedings of the First International Conference on Simulation of Adaptive Behavior (SAB90).
Marín, J. and Solé, R. V. (1999) Macroevolutionary Algorithms: A New Optimization Method on Fitness Landscapes. IEEE Transactions on Evolutioanry Computation. V 3, N 4, 272–286.
Mataric, M. J. (1992) Integration of Representation into Goal Driven Behavior Based Robotics, IEEE Transactions on Robotics and Automation, Vol. 8, No. 3, 304–312.
Nolfi, S. (1997) Using Emergent Modularity to Develop Control Systems for Mobile Robots, Adaptive Behavior, Vol. 5, No. 3–4, pp. 343–363.
Pasemann, F.; Steinmetz, U.; Hülse, M. and Lara, B. (2001) Robot Control and the Evolution of Modular Neurodynamics, this volume.
Pfeifer, R. and Scheier, C. (1999) Understanding Intelligence, MIT Press.
Van de Velde, W. (1993) Towards Learning Robots, MIT Press, Cambridge, MA.
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Duro, R.J., Becerra, J.A. & Santos, J. Behavior reuse and virtual sensors in the evolution of complex behavior architectures. Theory Biosci. 120, 188–206 (2001). https://doi.org/10.1007/s12064-001-0018-8
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DOI: https://doi.org/10.1007/s12064-001-0018-8