A Qualitative Framework for Reasoning Under Uncertainty

智能体必须在一个不确定的世界中进行推理和行动。表示这种不确定性的标准方法是使用概率。 虽然概率有许多优点，但在某些情况下，它似乎不合适或不必要。 这个项目的长期目标是了解我们在多大程度上可以保持概率的好处，同时使用更多的定性方法。重点是一种信念的度量，称为可验证性度量，其中事件的可验证性只是某些偏序空间中的一个元素。 似然性测度概括了文献中最著名的方法，包括概率、可能性（基于模糊逻辑）和Dempster-Shafer信念函数。目前正在进行四项主要调查： (1)默认推理-使用可扩展性来为默认值赋予语义，例如“鸟通常（或通常）会飞”。 (2)信念动力学-使用可解释性来捕捉代理人的信念，并定义一个适当的条件反射概念来捕捉这些信念如何随时间变化。 (3)定性决策理论-使用可验证性作为决策的定性方法的基础。 (4)复杂性问题-调查使用可解释性的定性推理是否比定量推理更容易。 这项研究应该有助于提供一个强大的工具，但易于使用，表示不确定性的框架。 人们希望，在不确定性下的定性和定量推理的组合将更有效地提供推理能力的智能代理比单独使用的方法。 ***

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. ***