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Neural networks for the EMOBOT robot control architecture

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Abstract

Within the EMOBOT approach to adaptive behaviour, the task of learning to control the behaviour is one of the most interesting challenges. Learned action selection between classically implemented control mechanisms, with respect to internal values and sensor readings, provides a way to modulate a variety of behavioural capabilities. To demonstrate the potential of the learning emotional controller, we chose a 10-5-12 MLP to implement the σ, α controller of the EMOBOT. Since no teacher vector is available for the chosen task, the neural network is trained with a reinforcement strategy. The emotion-value-dependent reinforcement signal, together with the output of the network, is the basis with which to compute an artificial teacher vector. Then, the established gradient descent method (backpropagation of error) is applied to train the neural network. First results obtained by extensive simulations show that a still unrevealed richness in behaviour can be realised when using the neural-network-based learning emotional controller.

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Acknowledgements

Part of this work is supported by the European Commission’s Information Society Technologies Programme, project SIGNAL, IST-2000-29225. Partners in this project are Napier University, National Research Council Genoa, Austrian Research Institute OFAI and the University of Bonn.

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Authors and Affiliations

  1. Division of Neural Computation, Department of Computer Science 6, University of Bonn, 53117, Bonn, Germany

    Nils Goerke, Timo Henne & Jürgen Müller

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  1. Nils Goerke
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  2. Timo Henne
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  3. Jürgen Müller
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Correspondence to Nils Goerke.

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Goerke, N., Henne, T. & Müller, J. Neural networks for the EMOBOT robot control architecture. Neural Comput & Applic 13, 299–308 (2004). https://doi.org/10.1007/s00521-004-0424-1

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  • DOI: https://doi.org/10.1007/s00521-004-0424-1

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