Abstract
Behaviours in autonomous agents – animals and robots – can be categorised according to whether they follow predetermined stimulus-response rules or make decisions based on explicit models of the world. Probability theory shows that optimal utility actions can in general be made only by considering all possible future states of world models. What is the relationship between rule-following and optimal utility modelling agents? We consider a particular case of an active mapping agent using two strategies: a simple wall-following rule and full Bayesian utility maximisation via entropy-based exploration. We show that for a class of environments generated by Ising models which include parameters modelling typical robotic mazes, the rule-following strategy tends to approximate optimal action selection but requires far less computation.
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Fox, C. (2013). Where Wall-Following Works: Case Study of Simple Heuristics vs. Optimal Exploratory Behaviour. In: Lepora, N.F., Mura, A., Krapp, H.G., Verschure, P.F.M.J., Prescott, T.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science(), vol 8064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39802-5_10
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DOI: https://doi.org/10.1007/978-3-642-39802-5_10
Publisher Name: Springer, Berlin, Heidelberg
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