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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)
In problem solving a goal/subgoal is either solved by generating needed information from current information, or further decomposed into additional subgoals. In traditional problem solving, goals, knowledge, and problem states are all modeled as expressions composed of symbolic predicates, and information generation is modeled as rule application based on(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)
We describe an architecture for exploring very large design spaces, for example, spaces that arise when design candidates are generated by combining components systematically from component libraries. A very large number of candidates are methodically considered and evaluated. This architecture is especially appropriate during the stage of conceptual design(More)