Efficient Semantic Tableau Generation for Abduction in

Abduction is a backward chaining inference, finding the best explanations of an observation with regard to a knowledge base in a two-steps process: i) hypotheses generation, and ii) explanations selection according to a minimality criterion. A propositional abduction problem P is a tuple 〈V,O, T 〉 [3], where V is a set of variables, T denotes the background knowledge of an application domain, O represents the observation, which is not directly entailed by the knowledge base T |= O, and H is a hypothesis of the problem P if T ∪ {H} |= O.