A New Stochastic Local Search Approach for Computing Preferred Extensions of Abstract Argumentation

摘要

In this paper, we proposed a new stochastic local search algorithm Inc-CCA_(EP) for computing the preferred extensions in (abstract) argumentation frameworks (AF). Inc-CCA_(EP) realizes an incremental version of Swcca, specially designed for computing the preferred extensions in AF. Experiments show that, Inc-CCA_(EP) notably outperforms the state-of-the-art solvers consistently on random benchmarks with nonempty preferred extensions.