Temporal Reasoning in Sequence Graphs

Temporal reasoning is widely used in AI. especially for natural language processing. Existing methods for temporal reasoning are extremely expensive in time and space. because complete graphs are used. We present an approach of temporal reasoning for expert systems in technical applications that reduces the amount of time and space by using sequence graphs. A sequence graph consists of one or more sequence chains and other intervals that are connected only loosely with these chains. Sequence chains are based on the observation that in technical applications many events occur sequentially. The uninterrupted execution of technical processes for a long time is characteristic for technical applications. To relate the first intervals in the application with the last ones makes no sense. In sequence graphs only these relations are stored that are needed for further propagation. In contrast to other algorithms which use incomplete graphs. no information is lost and the reduction of complexity is significant. Additionally. the representation is more transparent. because the "flow" of time is modelled.