Abstract
Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods which quickly converge to an approximate solution formedium-sized problems. Of this family HSVI, which uses trial-based asynchronous value iteration, can handle the largest domains. In this paper we suggest a new algorithm, FSVI, that uses the underlying MDP to traverse the belief space towards rewards, finding sequences of useful back-ups, and show how it scales up better than HSVI on larger benchmarks.
Original language | English |
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Pages (from-to) | 2619-2624 |
Number of pages | 6 |
Journal | IJCAI International Joint Conference on Artificial Intelligence |
State | Published - 1 Dec 2007 |
Event | 20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India Duration: 6 Jan 2007 → 12 Jan 2007 |
ASJC Scopus subject areas
- Artificial Intelligence