An empirical comparison of various representations of Dynamic Systems

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There exist several formalisms for representation and reasoning in dynamic systems, for example, Dynamic Influence Diagrams (DID), Influence Diagrams (ID), Dynamic Bayesian Networks (DBN), Bayesian Networks (BN), Hidden Markov Models (HMM), Markov Decision Processes (MDP), and Partially Observable Markov Decision Processes (POMDP). All these formalisms belong to graphical models based on probability theory. It has been shown that all probability models can be seen as variants of one generalization model. The purpose of this thesis is to review these models, to try to propose a unifying representation of these models at some generalization level (assuming DID level), and to test them in practice.