2017
IJCAI
IJCAI 2017
Unsatisfiable Core Shrinking for Anytime Answer Set Optimization
Abstract
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.
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Interdisciplinary Bridge
— Computer Science and Machine Learning
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Keyword Pioneer
— unsatisfiable core
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Hot Topic Early Bird
— model optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio