Extending the role of bias in probabilistic theory revision

Abstract Theory revision is the process of making corrections to a flawed or incomplete knowledge base on the basis of examples that expose those problems. The PTR algorithm is a theory revision algorithm that makes use of explicit bias to guide the detection of flawed knowledge base elements. In this paper, we examine the effectiveness of PTR's bias scheme in identifying flawed knowledge base elements, and we propose extensions to the PTR algorithm that support the use of additional bias to guide the process of correcting a flawed element once it has been located.