Copyright © PMLR 2017. All rights reserved. We study languages that specify Markov Decision Processes with Imprecise Probabilities (MDPIPs) by mixing probabilities and logic programming. We propose a novel language that can capture MDPIPs and Markov Decision Processes with Set-valued Transitions (MDPSTs); we then obtain the complexity of one-step inference for the resulting MDPIPs and MDPSTs. We also present results of independent interest on the complexity of inference with probabilistic logic programs containing interval-valued probabilistic assessments. Finally, we also discuss policy generation techniques.
|Original language||American English|
|Number of pages||12|
|State||Published - 1 Jan 2019|
|Event||Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 - |
Duration: 1 Jan 2019 → …
|Conference||Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017|
|Period||1/01/19 → …|