Abductive Markov Logic for Plan Recognition
Abstract
Plan recognition is a form of abductive reasoning that involves inferring plans that best explain sets of observed actions. Most existing approaches to plan recognition and other abductive tasks employ either purely logical methods that donot handle uncertainty, or purely probabilistic methods thatdo not handle structured representations. To overcome these limitations, this paper introduces an approach to abductive reasoning using a first-order probabilistic logic, specifically Markov Logic Networks (MLNs). It introduces several novel techniques for making MLNs efficient and effective for abduction. Experiments on three plan recognition datasets showthe benefit of our approach over existing methods.
Cite
Text
Singla and Mooney. "Abductive Markov Logic for Plan Recognition." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.8018Markdown
[Singla and Mooney. "Abductive Markov Logic for Plan Recognition." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/singla2011aaai-abductive/) doi:10.1609/AAAI.V25I1.8018BibTeX
@inproceedings{singla2011aaai-abductive,
title = {{Abductive Markov Logic for Plan Recognition}},
author = {Singla, Parag and Mooney, Raymond J.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2011},
pages = {1069-1075},
doi = {10.1609/AAAI.V25I1.8018},
url = {https://mlanthology.org/aaai/2011/singla2011aaai-abductive/}
}