  Logic-based abduction finds important applications in artificial intelligence
and related areas. One application example is in finding explanations for
observed phenomena. Propositional abduction is a restriction of abduction to
the propositional domain, and complexity-wise is in the second level of the
polynomial hierarchy. Recent work has shown that exploiting implicit hitting
sets and propositional satisfiability (SAT) solvers provides an efficient
approach for propositional abduction. This paper investigates this earlier work
and proposes a number of algorithmic improvements. These improvements are shown
to yield exponential reductions in the number of SAT solver calls. More
importantly, the experimental results show significant performance improvements
compared to the the best approaches for propositional abduction.
