The problem of representing temporal knowledge arises ill many areas of computer science. In applications in which such knowledge is imprecise or relative, current representations based on date lines or time instants are inadequate. An interval-based temporal logic is introduced, together WiUl a computationally effective reasoning algorithm based on constraint-propagation. This system is notable in offering a delicate balance between expressive power and the efficiency of its deductive engine. A notion of reference intervals is introduced which captures the temporal hierarchy implicit in many domains, and which can be used to precisely control the amount of deduction performed automatically by the system. Examples .are provided for a data base containing historical data, a d

The problem of representing temporal knowledge arises ill many areas of computer science. In applications in which such knowledge is imprecise or relative, current representations based on date lines or time instants are inadequate. An interval-based temporal logic is introduced, together WiUl a computationally effective reasoning algorithm based on constraint-propagation. This system is notable in offering a delicate balance between expressive power and the efficiency of its deductive engine. A notion of reference intervals is introduced which captures the temporal hierarchy implicit in many domains, and which can be used to precisely control the amount of deduction performed automatically by the system. Examples .are provided for a data base containing historical data, a d

No abstract

Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the 'Story Cloze Test'. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of 50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.

We present a representation of events and action based on interval temporal logic that is signi cantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their e ects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to di cult problems such as external events and simultaneous actions.