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Examines several fundamentals concerning the manner in which Markov decision problems may be properly formulated and the determination of solutions or their properties. Coverage includes optimal equations, algorithms and their characteristics, probability distributions, modern development in the Markov decision process area, namely structural policy analysis, approximation modeling, multiple objectives and Markov games. Copiously illustrated with examples.