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Partial Order Planning using Machine Learning Techniques

Babadi, Amin | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44692 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Ghassem-Sani, Gholamreza
  7. Abstract:
  8. Automated planning is a branch of artificial intelligence that studies intelligent agents’ decision making process.In planning, we can design agents that can decide on their own, about how to perform tasksthat are assigned to them. In classical planning, there is a restrictive assumption that actions in plans are totally ordered. By relaxing this restrictive assumption, partial order planninghas been created.Partial order planning uses a general principle, called the least commitment principle that results in a better performance than other classical planning methods. Yet, this branch of planning cannot compete with newer planning methods like heuristic search planning.That is why there has not been much attention to this branch of automated planning in the last few years. In this project, we have tried to improve one of the most successful partial order planning algorithms using machine learning techniques. We define the planning problem as a heuristic search problem. Then we introduce a new heuristic function that can be used in partial order planning. At the next step, we try to improve the quality of newly introduced heuristic function using a machine learning method.We have compared the performance of our planner, called LePop, with other well known partial order planners, like POP and VHPOP. Our resultshows that our method improves the performance of partial order planning in several planning domains
  9. Keywords:
  10. Machine Learning ; Classical Planning ; Partial Order ; Automatic Planning

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