Classical AI Planning Techniques for Problem Solving and Game Strategies
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Classical Planning
![General-Purpose Planning: State & Goals ,[object Object],[object Object],A C B C B A Initial state Goals ( Ke Xu )](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Classical AI Planning Techniques for Problem Solving and Game Strategies
- 4. Planning: Search Space A C B A B C A C B C B A B A C B A C B C A C A B A C B B C A A B C A B C A B C ( Michael Moll )
- 9. General Purpose vs. Domain-Specific General purpose : symbolic descriptions of the problems and the domain. The plan generation algorithm the same Domain Specific : The plan generation algorithm depends on the particular domain Advantage: - opportunity to have clear semantics Disadvantage: - symbolic description requirement Advantage: - can be very efficient Disadvantage: - lack of clear semantics - knowledge-engineering for plan generation Planning: find a sequence of actions to achieve a goal
- 14. Hierarchical (HTN) Planning Principle: Complex tasks are decomposed into simpler tasks. The goal is to decompose all the tasks into primitive tasks, which define actions that change the world. alternative methods Travel from UMD to Lehigh University Travel(UMD, Lehigh) Fly(National, L.V. International) Travel(L.V. Int’nal,Lehigh) Travel(UMD,National) Travel by car Enough money for gasoline Roads are passable Seats available Travel by plane Enough money for air fare available Taxi(UMD,UMD-Metro) Metro(UMD-Metro,National) Taxi(L.V. Int’nal,Lehigh)
- 18. Abstract Example Initial state final state Initial plan: Plan-space refinement State-space refinement Plan-space refinement State-space refinement