Utility-Based Abstraction and Categorization
This work addresses the challenge of managing complexity in automated reasoning systems, though it appears incremental by applying utility-based methods to an existing problem.
The paper tackles the problem of simplifying decision models in automated reasoning by introducing a utility-based approach to categorization, which clusters detailed states into abstract categories based on associated losses, and demonstrates this with the TUBA program.
We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show how we can cluster distinctions into groups of distinctions at progressively higher levels of abstraction, and describe rules for decision making with the abstractions. The techniques introduce a utility-based perspective on the nature of concepts, and provide a means of simplifying decision models used in automated reasoning systems. We demonstrate the techniques by describing the capabilities and output of TUBA, a program for utility-based abstraction.