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Logical Foundations for Cognitive Agents
We examine a number of techniques for representing actions with stochastic e ects using Bayesian networks and in uence diagrams. We compare these techniques according to ease of speci cation and size of the representation required for the complete speci cation of the dynamics of a particular system, paying particular attention the role of persistence relationships. We precisely characterize two components of the frame problem for Bayes nets and stochastic actions, propose several ways to dealdoi:10.1007/978-3-642-60211-5_5 fatcat:6qewp2zirzejznhvk72vovupbi