Dynamic programming in in uence diagrams with decision circuits

Decision circuits perform efficient evaluation of influence diagrams, building on the advances in arithmetic circuits for belief network inference [Darwiche, 2003; Bhattacharjya and Shachter, 2007]. We show how even more compact decision circuits can be constructed for dynamic programming in influence diagrams with separable value functions and conditionally independent subproblems. Once a decision circuit has been constructed based on the diagram’s “global” graphical structure, it can be compiled to exploit “local” structure for efficient evaluation and sensitivity analysis.