Probabilistic regions of persistence

Abstract

Perhaps the most difficult, and certainly the most intensely studied problem in temporal reasoning is the persistence of information-that is, what reasonable inferences can we draw about non-change given partial knowledge of the world and of the changes taking place. Almost all previous work hinges on McCarthy's common sense law of inertia (CSLI): things tend not to change. The obvious consequence of adopting this view is that it becomes reasonable to infer that the duration of non-change is arbitrarily long. For instance, a typical inference in systems that appeal to CSLI is that if a person is alive now, the person will remain alive (arbitrarily long) until something happens that results in the person's death.We describe a framework that allows a more realistic treatment of persistence by incorporating knowledge about the duration of persistence of information. Inferences, such as a wallet dropped on a busy street tends to remain where it fell for a shorter duration than a wallet lost on a hunting trip, can be drawn in this framework. Unlike the CSLI approach, this inference is possible without knowing what happened to change the wallet's location. We accomplish this by casting the problem of how long information persists as a problem in statistical reasoning.KeywordsTemporal LogicStatistical SemanticTypical InferenceFrame ProblemPartial KnowledgeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.