Abductive logic programming has been widely used to declar-atively specify a variety of problems in AI including updates in data and knowledge bases, belief revision, diagnosis, causal theory, and default reasoning. One of the most significant issues in abductive logic programming is to develop a reasonable method for knowledge assimilation, which incorporates obtained explanations into the current knowledge base. This paper offers a solution to this problem by considering disjunctive explanations whenever multiple explanations exist. Disjunctive explanations are then to be assimilated into the knowledge base so that the assimilated program preserves all and only minimal answer sets from the collection of all possible updated programs. We describe a new form of abductive logic programming which deals with disjunctive explanations in the framework of extended abduction. The proposed framework can be well applied to view updates in disjunctive databases.
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