A Computational Theory of Decision Networks

This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. Fishburn 1988). A general representation named decision networks is proposed based on influence diagrams (Howard and Matheson 1984). This new representation incorporates the idea, from Markov decision processes (e.g. Puterman 1990, Denardo 1982), that a decision may be conditionally independent of certain pieces of available information. It also allows multiple cooperative agents and facilitates the exploitation of separability in the utility function. Decision networks inherit the advantages of both influence diagrams and Markov decision processes