The problem of abduction can be characterized as finding the best explanation of a set of data. In this paper we focus on one type of abduction in which the best explanation is the most plausible combination of hypotheses that explains all the data. We then present several computational complexity results demonstrating that this type of abduction is intractable (NP-hard) in general. In particular, choosing between incompatible hypotheses, reasoning about cancellation effects among hypotheses, and satisfying the maximum plausibility requirement are major factors leading to intractability. We also identify a tractable, but restricted, class of abduction problems. Thanks to B. Chandrasekaran, Ashok Goel, Jack Smith, and Jon Sticklen for their comments on the numerous versions of this paper. The referees have also made a substantial contribution. Any remaining errors are our responsibility, of course. This research has been supported in part by the National Library of Medicine, grant LM-...

In the following report, we describe a new shell for building abductive problem solving agents. This shell, called Peirce-IGTT 1 , can be used in conjunction with other problem solving tools constructed at the Laboratory for Artificial Intelligence Research at The Ohio State University in order to build large knowledgebased systems. Peirce itself is a tool for building agents to solve the abductive tasks of hypothesis assembly and critique. This report will discuss the Peirce tool and its algorithm, some brief history of the construction of the tool and a sample case that was constructed from it. Abductive Problem Solving Abduction is inference to the best explanation[9]. It is a form of problem solving where hypotheses are formed and selected to explain a set of data or findings. This form of inference can be used in solving diagnostic and related sort of problems[5, 6, 12, 14, 19]. In attempting to solve an abductive problem, there generally needs to be a means to generate hypothe...