Exploiting the Rule Structure for Decision Making within the Independent Choice Logic
This work addresses a bottleneck in influence diagram evaluation for AI and decision theory, offering a method to cut down combinatorics, though it appears incremental as it builds on existing logical and stochastic frameworks.
The paper tackles the combinatorial explosion in dynamic programming for decision-making under uncertainty by introducing the Independent Choice Logic (ICLdt), a framework that extends logic programming and stochastic models. It shows that exploiting the rule-based structure of ICLdt allows distinctions only in information values that affect utility, reducing computational complexity.
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.