Unsatisfiable Core Shrinking for Anytime Answer Set Optimization

Efficient algorithms for the computation of optimum stable models are based on unsatisfiable core analysis. However, these algorithms essentially run to completion, providing few or even no suboptimal stable models. This drawback can be circumvented by shrinking unsatisfiable cores. Interestingly, the resulting anytime algorithm can solve more instances than the original algorithm.

Authors

• Mario Alviano
• Carmine Dodaro

Keywords

• Artificial Intelligence: artificial intelligence
• Artificial Intelligence: knowledge representation and reasoning