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.
|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