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The problem of abduction can be characterized as nding 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(More)
Much of the work on ontologies in AI has focused on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. A goal is to make knowledge sharable, by encoding domain knowledge using a standard vocabulary based on the ontology. A parallel attempt at identifying the ontology of problem-solving knowledge has(More)
The problem of finding a best explanation of a set of data has been a topic of much interest in Artificial Intelligence. In this paper we present an approach to this problem by hypothesis assembly. We present this approach formally so that we can examine the time complexity and correctness of the algorithms. We then examine a system implemented using this(More)