The Bayesian inference (or Free Energy Minimization) view on decision making and goal-directed behaviour

Classical Computer Science approaches to behavioural problems -- such as Reinforcement Learning, control and behaviour planning -- are dominated by Bellman's principles and dynamic programming. However, some fundamental questions are hard to address in this framework: How can planning and decision making be realized on distributed representations? How on hierarchies and mixed (discrete, continuous) representations? How can appropriate representations be learnt? And what is a coherent computational paradigm that solves behavioural problems equally to state estimation and sensor processing problems? Recently there have been a series of papers showing that behavioural problems can be reformulated as a problem of Bayesian inference or Free Energy Minimization in graphical models where actions, states, observations and rewards are equally represented as coupled random variables. The most important implication of this view is that existing Machine Learning methods such as inference on factored and hierarchical representations, likelihood maximization, and unsupervised learning (which were classically associated to sensor processing and learning sensor representations) can now be transferred to the realm of behaviour organization. In this talk I will introduce to the general approach and give examples from our recent work. The focus will be on discussing the concept, its premises and limitations, and relations to other models of goal-directed behaviour.