Consequence-based and automata-based algorithms encompass two families of approaches that have been thoroughly studied as reasoning methods for many logical formalisms. While automata are useful for finding tight complexity bounds, consequence-based algorithms are typically simpler to describe, implement, and optimize. In this paper, we show that consequence-based reasoning can be reduced to the emptiness test of an appropriately built automaton. Thanks to this reduction, one can focus on developing efficient consequence-based algorithms, obtaining complexity bounds and other benefits of automata methods for free.
CITATION STYLE
Hutschenreiter, L., & Peñaloza, R. (2017). An automata view to goal-directed methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10168 LNCS, pp. 103–114). Springer Verlag. https://doi.org/10.1007/978-3-319-53733-7_7
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