We consider online partially observable Markov decision processes (POMDPs) which compute policies by local look-ahead from the current belief-state. One problem is that belief-nodes deeper in the decision-tree increase in the number of states with non-zero probability they contain. Computation time of updating a belief-state is exponential in the number of states contained by the belief. Belief-update occurs for each node in a search tree. It would thus pay to reduce the size of the nodes while keeping the information they contain. In this paper, we compare four fast and frugal methods to reduce the size of belief-nodes in the search tree, hence improving the running-time of online POMDP algorithms.
Reference:
Rens, G and Ferrein, A. 2013. Belief-node Condensation for Online POMDP Algorithms. In: IEEE AFRICON 2013, Mauritius, 9-12 September 2013
Rens, G., & Ferrein, A. (2013). Belief-node Condensation for Online POMDP Algorithms. Centre for Artificial Intelligence Research. http://hdl.handle.net/10204/7190
Rens, G, and A Ferrein. "Belief-node Condensation for Online POMDP Algorithms." (2013): http://hdl.handle.net/10204/7190
Rens G, Ferrein A, Belief-node Condensation for Online POMDP Algorithms; Centre for Artificial Intelligence Research; 2013. http://hdl.handle.net/10204/7190 .