ON THE GENERATION OF MARKOV DECISION-PROCESSES

Comparisons of the performance of solution algorithms for Markov decis ion processes rely heavily on problem generators to provide sizeable s ets of test problems. Existing generation techniques allow little cont rol over the properties of the test problems and often result in probl ems which are not typical of real-world examples. This paper identifie s the properties of Markov decision processes which affect the perform ance of solution algorithms, and also describes a new problem generati on technique which allows all of these properties to be controlled.