Efficient Solutions to Factored MDPs with Imprecise Transition Probabilities

When modeling real-world decision-theoretic planning problems in the Markov decision process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MDP transition models from an expert or data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while solutions to the MDP-IP are well-known, they require nonlinear optimization and are extremely time-consuming in practice. To address this deficiency, we propose efficient dynamic programming methods to exploit the structure of factored MDPIPs. Noting that the key computational bottleneck in the solution of MDP-IPs is the need to repeatedly so...

• Type: Conference
• Year: 2009
• Where: AIPS
• Authors: Karina Valdivia Delgado, Scott Sanner, Leliane Nunes de Barros, Fabio Gagliardi Cozman
• Keywords: AIPS 2009, Artificial Intelligence, Markov Decision Process, MDP Transition Models, Transition Probabilities