Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1058-1064.
Abstract
Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. HEX-programs extend ASP with external atoms for access to arbitrary external information. In this work, we extend the evaluation principles of external atoms to partial assignments, lift nogood learning to this setting, and introduce a variant of nogood minimization. This enables external sources to guide the search for answer sets akin to theory propagation. Our benchmark experiments demonstrate a clear improvement in efficiency over the state-of-the-art HEX-solver.
@inproceedings{ijcai2016p154,
title = {Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access},
author = {Eiter, Thomas and Kaminski, Tobias and Redl, Christoph and Weinzierl, Antonius},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {1058--1064},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/154},
}
TY - CPAPER TI - Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access AU - Eiter, Thomas AU - Kaminski, Tobias AU - Redl, Christoph AU - Weinzierl, Antonius T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 1058 EP - 1064 UR - https://ijcai.org/proceedings/2016/154 ER -Copied
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