Anytime Default Inference

Default reasoning plays a fundamental role in a variety of information processing applications. Default inference is inherently computa-tionally hard and practical applications, specially time-bounded ones, may require that some notion of approximate inference be used. Anytime algorithms are a useful conceptualization of processes that may be prematurely terminated whenever necessary to return useful partial answers, with the quality of the answers improving in a well-deened manner with time. In this paper, we develop a repertoire of meaningful partial solutions for default inference problems and use these as the basis for specifying general classes of anytime default inference algorithms. We then present some of our earlier results on the connection between partial constraint satisfaction and default reasoning and exploit this connection to identify a large space of possible algorithms for default inference that may be deened based on partial constraint satisfaction techniques, which are inherently anytime in nature. The connection is useful because a number of existing techniques from the area of partial constraint satisfaction can be applied with little or no modiication to default inference problems and because tractable cases for partial constraint satisfaction suggest tractable default inference problems.