  This paper introduces the independent choice logic, and in particular the
"single agent with nature" instance of the independent choice logic, namely
ICLdt. This is a logical framework for decision making uncertainty that extends
both logic programming and stochastic models such as influence diagrams. This
paper shows how the representation of a decision problem within the independent
choice logic can be exploited to cut down the combinatorics of dynamic
programming. One of the main problems with influence diagram evaluation
techniques 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 the
ICLdt can be exploited so that we only make distinctions in the values of the
information available for a decision that will make a difference to utility.
