Improving Determinization in Hindsight for On-line Probabilistic Planning

Recently, `determinization in hindsight' has enjoyed surprising success in on-line probabilistic planning. This technique evaluates the actions available in the current state by using non-probabilistic planning in deterministic approximations of the original domain. Although the approach has proven itself effective in many challenging domains, it is computationally very expensive. In this paper, we present three significant improvements to help mitigate this expense. First, we use a method for detecting potentially useful actions, allowing us to avoid estimating the values of unnecessary ones. Second, we exploit determinism in the domain by reusing relevant plans rather than computing new ones. Third, we improve action evaluation by increasing the chance that at least one deterministic plan reaches a goal. Taken together, these improvements allow determinization in hindsight to scale significantly better on large or mostly-deterministic problems.

• Type: Conference
• Year: 2010
• Where: AIPS
• Authors: Sung Wook Yoon, Wheeler Ruml, J. Benton, Minh Do
• Keywords: AIPS 2010, Artificial Intelligence, Deterministic Approximations, On-line Probabilistic Planning, Surprising Success