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This paper introduces the independent choice logic, and in particular the"single agent with nature" instance of the independent choice logic, namelyICLdt. This is a logical framework for decision making uncertainty that extendsboth logic programming and stochastic models such as influence diagrams. Thispaper shows how the representation of a decision problem within the independentchoice logic can be exploited to cut down the combinatorics of dynamicprogramming. One of the main problems with influence diagram evaluationtechniques is the need to optimise a decision for all values of the 'parents'of a decision variable. In this paper we show how the rule based nature of theICLdt can be exploited so that we only make distinctions in the values of theinformation available for a decision that will make a difference to utility.

Exploiting the Rule Structure for Decision Making within the Independent Choice Logic