Abstract

Reasoning about actions has been a focus of interest in AI from the beginning and continues to receive attention. But the range of situations considered has been rather narrow and falls well short of what is needed for understanding natural language. Language understanding requires sophisticated reasoning about actions and events and the world's languages employ a variety of grammatical and lexical devices to construe, direct attention and focus on, and control inferences about actions and events. We implemented a neurally inspired computational model that is able to reason about linguistic action and event descriptions, such as those found in news stories. The system uses an active event representation that also seems to provide natural and cognitively motivated solutions to classical problems in logical theories of reasoning about actions. For logical approaches to reasoning about actions, we suggest that looking at story understanding sets up fairly strong desiderata both in terms of the ne-grained event and action distinctions and the kinds of real-time inferences required. 1

Citations