The Compilation of Decision Models
This work addresses decision-making efficiency in engineering settings, but it appears incremental as it builds on existing decision-theoretic frameworks without introducing major breakthroughs.
The paper tackles the problem of compiling decision models by selecting optimal subsets of evidence for situation-action mappings, focusing on binary decisions with conditional independence. It presents methods for incremental evidence selection and analyzes the relationship between evidence weight distributions and compilation preferences.
We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective. The techniques described allow us to evaluate various configurations of compiled knowledge given the nature of evidential relationships in a domain, the utilities associated with alternative actions, the costs of run-time delays, and the costs of memory. We describe procedures for selecting a subset of the total observations available to be incorporated into a compiled situation-action mapping, in the context of a binary decision with conditional independence of evidence. The methods allow us to incrementally select the best pieces of evidence to add to the set of compiled knowledge in an engineering setting. After presenting several approaches to compilation, we exercise one of the methods to provide insight into the relationship between the distribution over weights of evidence and the preferred degree of compilation.