Partially Observable Markov Decision Process (POMDP). The ’pomdp-solve’ program solves problems that are formulated as partially observable Markov decision processes, a.k.a. POMDPs. It uses the basic dynamic programming approach for all algorithms, solving one stage at a time working backwards in time. It does finite horizon problems with or without discounting. It will stop solving if the answer is within a tolerable range of the infinite horizon answer, and there are a couple of different stopping conditions (requires a discount factor less than 1.0). Alternatively you can solve a finite horizon problem for some fixed horizon length. The code actually implements a number of POMDP solution algorithms