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FF-Replan was the winner of the 2004 International Proba-bilistic Planning Competition (IPPC-04) (Younes & Littman 2004a) and was also the top performer on IPPC-06 domains, though it was not an official entry. This success was quite surprising , due to the simplicity of the approach. In particular, FF-Replan calls FF on a carefully constructed deterministic(More)
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual value-function learning step with a learning step in policy space. This is advantageous in domains where good policies are easier to represent and learn than the corresponding(More)
We explore approximate policy iteration (API), replacing the usual cost-function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical(More)
This paper investigates hindsight optimization as an approach for leveraging the significant advances in deterministic planning for action selection in probabilistic domains. Hindsight optimization is an online technique that evaluates the one-step-reachable states by sampling future outcomes to generate multiple non-stationary deterministic planning(More)
A number of today's state-of-the-art planners are based on forward state-space search. The impressive performance can be attributed to progress in computing domain independent heuristics that perform well across many domains. However, it is easy to find domains where such heuristics provide poor guidance, leading to planning failure. Motivated by such(More)
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(More)
OBJECTIVE The primary purpose of this study was to investigate the differences in the serum brain-derived neurotrophic factor (BDNF) level between elderly Korean people over 65 years with and without dementia. METHODS 171 individuals over 65 years were enrolled in this study. Screening for cognitive impairments was carried out using the Mini-Mental Status(More)
We describe and evaluate a system for learning domain-specific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well on " long random walk " problem distributions. The system is based on viewing planning domains as very large Markov decision processes and then applying a recent variant of(More)