Utility-Based Abstraction and Categorization

TL;DR

This paper introduces a utility-based approach to categorization that simplifies decision models by clustering detailed states into abstract categories, enhancing automated reasoning systems.

Contribution

It presents a novel utility-based method for abstraction and categorization, including clustering techniques and decision rules, demonstrated through the TUBA system.

Findings

Effective clustering of states into higher-level categories

Simplification of decision models in automated reasoning

Demonstrated capabilities of the TUBA system

Abstract

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.