Weighted Abduction For Plan Ascription

Abstract

We describe an approach to abductive reasoning called weighted abduction, which uses inference weights to compare competing explanations for observed behavior. We present an algorithm for computing a weighted-abductive explanation, and sketch a model-theoretic semantics for weighted abduction. We argue that this approach is well suited to problems of reasoning about mental state. In particular, we show how the model of plan ascription developed by Konolige and Pollack can be recast in the framework of weighted abduction, and we discuss the potential advantages and disadvantages of this encoding.