Planning With Deadlines in Stochastic Domains

Planning With Deadlines in Stochastic Domains (1993)

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by Thomas Dean , Leslie Pack Kaelbling , Jak Kirman , Ann Nicholson

Venue:	In Proceedings of the Eleventh National Conference on Artificial Intelligence
Citations:	125 - 10 self

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Datum	Value	Source
TITLE	Planning With Deadlines in Stochastic Domains	user correction - Legacy Corrections
AUTHOR NAME	Thomas Dean	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Brown University, Providence	user correction - Legacy Corrections
AUTHOR ADDR	RI 02912	user correction - Legacy Corrections
AUTHOR NAME	Leslie Pack Kaelbling	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Brown University, Providence	user correction - Legacy Corrections
AUTHOR ADDR	RI 02912	user correction - Legacy Corrections
AUTHOR NAME	Jak Kirman	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Brown University, Providence	user correction - Legacy Corrections
AUTHOR ADDR	RI 02912	user correction - Legacy Corrections
AUTHOR NAME	Ann Nicholson	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Brown University, Providence	user correction - Legacy Corrections
AUTHOR ADDR	RI 02912	user correction - Legacy Corrections
ABSTRACT	We provide a method, based on the theory of Markov decision problems, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards. Standard goals of achievement, as well as goals of maintenance and prioritized combinations of goals, can be specified in this way. An optimal policy can be found using existing methods, but these methods are at best polynomial in the number of states in the domain, where the number of states is exponential in the number of propositions (or state variables) . By using information about the starting state, the reward function, and the transition probabilities of the domain, we can restrict the planner's attention to a set of world states that are likely to be encountered in satisfying the goal. Furthermore, the planner can generate more or less complete plans depending on the time it has avail...	user correction - Legacy Corrections
YEAR	1993	INFERENCE
VENUE	In Proceedings of the Eleventh National Conference on Artificial Intelligence	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	574--579	INFERENCE
CITATIONS	12 found	ParsCit 1.0