The role of essential explanation in abduction

摘要

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.