Agents must reason and act in an uncertain world. The standard approach to representing this uncertainty is to use probability. While probability has many advantages, there are situations where it seems inappropriate or unnecessary. The long-term goal of this project is to understand to what extent we can maintain the benefits of probability while using more qualitative approaches. The focus is on one measure of belief, called a plausibility measure, where the plausibility of an event is just an element in some partially-ordered space. Plausibility measures generalize the bestknown approaches in the literature, including probability, possibility (based on fuzzy logic), and Dempster-Shafer belief functions. Four main lines of investigation are being pursued: (1) Default reasoning---using plausibility to give semantics to defaults such ``birds typically (or normally) fly''. (2) Belief dynamics---using plausibility to capture an agent's beliefs, and defining an appropriate notion of conditioning to capture how these beliefs change over time. (3) Qualitative decision theory---using plausibility as a basis for a qualitative approach to decision making. (4) Complexity issues---investigating whether qualitative reasoning using plausibility is easier than more quantitative reasoning. This research should help provide tools for a powerful, yet easy-touse, framework for representing uncertainty. It is hoped that a combination of qualitative and quantitative reasoning under uncertainty will be more effective in providing reasoning ability to intelligent agents than either approach used alone. ***
