A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916314398851072 |
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| author | Muppasani, Bharath Pallagani, Vishal Srivastava, Biplav Mutharaju, Raghava Huhns, Michael N. Narayanan, Vignesh |
| author_facet | Muppasani, Bharath Pallagani, Vishal Srivastava, Biplav Mutharaju, Raghava Huhns, Michael N. Narayanan, Vignesh |
| contents | Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_13549 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency Muppasani, Bharath Pallagani, Vishal Srivastava, Biplav Mutharaju, Raghava Huhns, Michael N. Narayanan, Vignesh Artificial Intelligence Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research. |
| title | A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2307.13549 |