Abstract
Truth maintenance (also called belief revision or reason maintenance) is an area of AI concerned with revising sets of beliefs and maintaining the truth in the system when new information contradicts existing information. Truth maintenance systems (TMSs) work with inference engines that act as problem solvers within large search spaces. The inference engine explores alternatives, makes choices, and examines the consequences of the choices. If a contradiction is detected during this process, the TMS eliminates it by revising the knowledge base. Together, the TMS and inference engine can solve problems where algorithmic solutions don't exist, and thus offer an efficient way to deal with search spaces that are large due to combinatorial explosions of alternatives. TMSs can be implemented either explicitly in search problem solving tools, or implicitly within applications that solve particular search problems. All applications that solve search problems have something in common that can be extracted and implemented within a tool. This tool can then solve new problems without programming everything from scratch. >
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