Connections between default reasoning and partial constraint satisfaction

This paper provides the foundation of connections between default reasoning and constraint satisfaction. Such connections are important because they combine fields with different strengths that complement each other: default reasoning is broadly seen as a promising method for reasoning from incomplete information, but is hard to implement. On the other hand, constraint satisfaction has evolved as a powerful, and efficiently implementable, problem solving paradigm in artificial intelligence. In this paper, we show how THEORIST knowledge bases and theories in Constrained Default Logic with prerequisite-free defaults may be mapped to partial constrained satisfaction problems. We also extend these results to deal with priorities among defaults.