Distributed revision of composite beliefs
Artificial Intelligence
Introduction to artificial intelligence
Introduction to artificial intelligence
Motivation analysis, abductive unification, and nonmonotonic equality
Artificial Intelligence
Some results concerning the computational complexity of abduction
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Abductive explanation: on why the essentials are essential
Methodologies for intelligent systems, 5
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
On the mechanization of abductive logic
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Computational complexity of hypothesis assembly
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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The abduction task is to infer the best explanation for a given set of data. One common subtask of abduction is to synthesize the best composite explanatory hypothesis from elementary hypotheses retrieved from memory. The synthesis of best composite explanations, however, is computationally costly. One general approach to controlling the computational cost of synthesizing explanations is to decompose the synthesis search space into smaller spaces that can be searched more efficiently and effectively. The essential hypotheses, that is, the hypotheses that are the only available explanations for specific subsets of the data set, provide one such decomposition. In this method, first the essential hypotheses are included in the composite explanation, and, then, non-essential hypotheses are included to account for the remaining unexplained data elements. In addition to providing a more efficient method for synthesizing composite explanations, this decomposition leads to the formation of more parsimonious explanations. In this paper, we report on a set of experiments in the domain of medical data interpretation that demonstrates that the essential/non-essential decomposition of the abduction search space results in more efficient synthesis of more parsimonious composite explanations.