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Abductive markov logic for plan recognition
| Content Provider | Semantic Scholar |
|---|---|
| Author | Singla, Parag Mooney, Raymond J. |
| Copyright Year | 2011 |
| 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 do not handle uncertainty, or purely probabilistic methods that do 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 show the benefit of our approach over existing methods. |
| Starting Page | 1069 |
| Ending Page | 1075 |
| Page Count | 7 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.utexas.edu/users/ai-lab/downloadPublication.php?filename=http://www.cs.utexas.edu/users/ml/papers/singla.aaai11.pdf&pubid=127066 |
| Alternate Webpage(s) | http://www.cs.utexas.edu/~ai-lab/downloadPublication.php?filename=http://www.cs.utexas.edu/users/ml/papers/singla.aaai11.pdf&pubid=127066 |
| Journal | AAAI 2011 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
