Abstract
Simplifying classical planning tasks by removing operators while preserving at least one optimal solution can significantly enhance the performance of planners. In this paper, we introduce the notion of operator mutex, which is a set of operators that cannot all be part of the same (strongly) optimal plan. We propose four different methods for inference of operator mutexes and experimentally verify that they can be found in a sizable number of planning tasks. We show how operator mutexes can be used in combination with structural symmetries to safely remove operators from the planning task.
| Original language | English |
|---|---|
| Title of host publication | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
| Publisher | AAAI press |
| Pages | 7586-7593 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781577358091 |
| DOIs | |
| State | Published - 2019 |
| Externally published | Yes |
| Event | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - Honolulu, United States Duration: 27 Jan 2019 → 1 Feb 2019 |
Publication series
| Name | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
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Conference
| Conference | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
|---|---|
| Country/Territory | United States |
| City | Honolulu |
| Period | 27/01/19 → 1/02/19 |
Bibliographical note
Publisher Copyright:© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
The work of Daniel Fisˇer was supported by the Czech Science Foundation (grant no. 18-24965Y and 18-07252S). The work of Alexander Shleyfman was supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”. The work of Daniel Fi?er was supported by the Czech Science Foundation (grant no. 18-24965Y and 18-07252S). The work of Alexander Shleyfman was supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme ?Projects of Large Research, Development, and Innovations Infrastructures?.
| Funders | Funder number |
|---|---|
| Grantová Agentura České Republiky | 18-24965Y, 18-07252S |
| Israel Academy of Sciences and Humanities | LM2015042 |