Title :
An approximate algorithm, with bounds, for composite-state partially observed Markov decision processes
Author :
Lovejoy, William S.
Author_Institution :
Graduate Sch. of Bus., Stanford Univ., CA, USA
Abstract :
The author presents an approximate algorithm with bounds, for solving composite-state POMDPs (partially observed Markov decision processes). The approximation is based on a discretization of the unit simplex that has proven effective with conventional POMDPs. The model considered is a composite-state space variation of the discrete-time, finite partially observed Markov decision process with stationary cost data analyzed by R.D. Smallwood and E.J. Sondik (1973). The computational savings achievable with the composite-state construction are indicated
Keywords :
Markov processes; decision theory; state-space methods; Markov decision processes; approximate algorithm; composite-state; decision theory; discretization; unit simplex; Artificial intelligence; Costs; Data analysis; History; Probability distribution; Space stations; State estimation; State-space methods;
Conference_Titel :
Decision and Control, 1990., Proceedings of the 29th IEEE Conference on
Conference_Location :
Honolulu, HI
DOI :
10.1109/CDC.1990.203826
