Nonmonotonic reasoning and logic programming are both areas of crucial and growing significance to artificial intelligence and to the whole field of computer science. Nevertheless, despite the close realtionship between the two areas, they have been developed largely independently of each other. However, the recent discovery of the equivalence between the perfect model semantic of logic programs and natural forms of all four major formalizations of nonmonotonic reasoning establishes a closer link between the two areas and paves the way for using efficient computation methods, developed for logic programming, as inference engines for nonmonotinic reasoning. The problem of finding efficient inference mechanisms that can reason in a commonsense manner in the absence of complete information is one of the major research and implementation problems in artificial intelligence. To permit the use of logic programming as such an inference engine, the investigator will develop effective procedural mechanisms for efficient execution of such methods, and will implement an experimental nonmonotonic reasoning system, based on logic programming.
