Uncertainty in Artificial Intelligence
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RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains
Emma Brunskill, Stuart Russell
Despite the intractability of generic optimal partially observable Markov decision process planning, there exist important problems that have highly structured models. Previous researchers have used this insight to construct more efficient algorithms for factored domains, and for domains with topological structure in the flat state dynamics model. In our work, motivated by findings from the education community relevant to automated tutoring, we consider problems that exhibit a form of topological structure in the factored dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains (RAPID) leverages this structure to efficiently compute a good initial envelope of reachable states under the optimal MDP policy in time linear in the number of state variables. RAPID performs partially-observable planning over the limited envelope of states, and slowly expands the state space considered as time allows. RAPID performs well on a large tutoring-inspired problem simulation with 122 state variables, corresponding to a flat state space of over 10^30 states.
Pages: 83-92
PS Link:
PDF Link: /papers/10/p83-brunskill.pdf
AUTHOR = "Emma Brunskill and Stuart Russell",
TITLE = "RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "83--92"

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