@inproceedings{fcf82d8634b848faaa49805b4e4a5b86,
title = "Weight learning in a probabilistic extension of answer set programs",
abstract = "LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved. In this paper, we present the concept of weight learning in LPMLN and learning algorithms for LPMLN derived from those for Markov Logic. We also present a prototype implementation that uses answer set solvers for learning as well as some example domains that illustrate distinct features of LPMLN learning. Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains. We also apply the method to learn the parameters for probabilistic abductive reasoning about actions.",
author = "Joohyung Lee and Yi Wang",
note = "Funding Information: Acknowledgments: We are grateful to Zhun Yang and the anonymous referees for their useful comments. This work was partially supported by the National Science Foundation under Grants IIS-1526301 and IIS-1815337. Publisher Copyright: Copyright {\textcopyright} 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 16th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2018 ; Conference date: 30-10-2018 Through 02-11-2018",
year = "2018",
language = "English (US)",
series = "Principles of Knowledge Representation and Reasoning: Proceedings of the 16th International Conference, KR 2018",
publisher = "AAAI press",
pages = "22--31",
editor = "Michael Thielscher and Francesca Toni and Frank Wolter",
booktitle = "Principles of Knowledge Representation and Reasoning",
}