Decision-theoretic Refinement Planning Using Inheritance Abstraction

plan’s outcomes are sets of outcomes of more those that might be refinable to the optimal plan. The concrete plans. Since different probability and utility values mav be associated with each specific outcome.. in general a probability range and a utility range will be associated with each abstract outcome. Thus the expected utility of an abstract plan is represented by an interval, which includes the expected utilities of all possible instantiations of that abstract plan. Refining the plan, i. e., instantiating one of its actions, tends to narrow the interval. Whenthe expected utility intervals of two plans do not overlap, the one with the lower 1intcrval can be eliminated. Wepresent a method for abstracting probabilistic conditional actions and we show how to compute expected utility bounds for plans containing such abstract actions. Wepresent a planning algorithm that *This work was inspired by discussions with Steve Hanks. Manonton Butaxbutar performed the complexity analysis of the algorithm. This work was partially supported by NSFgrant #IRI-9207262. 1If the upper boundon the expected utility of each abstract plan is tight, i. e., there is an instanceof the abstract plan with that expected utility, then at each refinement step we can eliminate all abstract plans but the one with the highest upper bound. 266 POSTERS From: AIPS 1994 Proceedings. Copyright © 1994, AAAI (www. aaai. org). All deli sati i live func degr pone DSAf comp temp 0 2 tons 85 155time the of a UR bini DSAa dead rese a go that over resi F 2. 5 4. 5 fed ourt from Figure 2: Specification of delivery utility function. woul tons bene

Authors

• Peter Haddawy
• Meliani Suwandi

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