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Markov Decision Processes (MDPs) are a classical framework for
stochastic sequential decision problems, based on an enumerated state
space representation. More compact and structured representations have
been proposed: factorization techniques use state variables
representations, while decomposition techniques are based on a
partition of the state space into sub-regions and take advantage of
the resulting structure of the state transition graph. We use a family
of probabilistic exploration-like...
Random forest is an ensemble method of machine learning that reaches a high level of accuracy in decision-making but is difficult to understand from the point of view of interpreting local or global decisions. In the article, we use this method as a means to analyze the edge 3-colorability of cubic graphs and to find the properties of the graphs that affect it most strongly. The main contributions of the presented research are four original datasets suitable for machine learning methods, a random...
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