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- California Institute of TechnologyUS46 papers
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4 papers · 2 filters
Planning under Continuous Time and Resource Uncertainty: A Challenge for AI
John Bresina, Richard Dearden, Nicolas Meuleau +3
We outline a class of problems, typical of Mars rover operations, that are problematic for current methods of planning under uncertainty. The existing methods fail because they suf…
Optimal Limited Contingency Planning
Nicolas Meuleau, David Smith
For a given problem, the optimal Markov policy can be considerred as a conditional or contingent plan containing a (potentially large) number of branches. Unfortunately, there are…
Dynamic Programming for Structured Continuous Markov Decision Problems
Zhengzhu Feng, Richard Dearden, Nicolas Meuleau +1
We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamical…
Predicting the behavior of interacting humans by fusing data from multiple sources
Erik J. Schlicht, Ritchie Lee, David H. Wolpert +2
Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost.…