Abduction in temporal reasoning

Abstract

Commonsense knowledge often omits the temporal incidence of facts, and even the ordering between occurrences is only available for some of their instances. Reasoning about the temporal extent of facts and their sequencing becomes complex due to this inherent partiality. The generation of hypotheses is adopted here as a natural way to overcome the difficulties in computing answers to temporal queries. The proposed abductive system performs temporal reasoning in a logic programming framework. Queries are taken as goals and the inference system combines deduction with abduction and constraint solving. The convenience of constraints for dealing with temporal information is widely recognized, their interest being twofold: the representation of essential properties of time and the provision for partial information, allowing flexible bounds on times instead of constant bindings. Inference manipulates a language associating propositions with time periods which are maximal intervals for the proposition. The abductive inference procedure is described here, identifying the constraint operations required. It is also shown that the outcome of a derivation is always consistent with the information in the knowledge base.