Learning and fatigue inspired method for optimized HTN planning
Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN...
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| Veröffentlicht in: | Journal of systems engineering and electronics 2012-04, Vol.23 (2), p.233-241 |
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| container_title | Journal of systems engineering and electronics |
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| creator | Zhang, Wanpeng Shen, Lincheng Chen, Jing |
| description | Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method. |
| doi_str_mv | 10.1109/JSEE.2012.00030 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 1004-4132 |
| ispartof | Journal of systems engineering and electronics, 2012-04, Vol.23 (2), p.233-241 |
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| language | eng |
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| source | IEEE Power & Energy Library; EZB-FREE-00999 freely available EZB journals |
| subjects | Electronics Exploration Fatigue (materials) Learning Mathematical models Networks Searching Tasks 优化 学习方法 搜索过程 智能规划 深度优先搜索 灵感 疲劳机制 网络规划 |
| title | Learning and fatigue inspired method for optimized HTN planning |
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