Arguing about beliefs and actions

Decision making under uncertainty is central to reasoning by practical intelligent systems, and attracts great controversy. The most widely accepted approach is to represent uncertainty in terms of prior and conditional probabilities of events and the utilities of consequences of actions, and to apply standard decision theory to calculate degrees of belief and expected utilities of actions. Unfortunately, as has been observed many times, reliable probabilities are often not easily available. Furthermore the benefits of a quantitative probabilistic representation can be small by comparison with the restrictions imposed by the formalism. In this paper we summarise an approach to reasoning under uncertainty by constructing arguments for and against particular options and then describe an extension of this approach to reasoning about the expected values of actions.