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AAAI
2010
15 years 8 months ago
Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search
Perfect Information Monte Carlo (PIMC) search is a practical technique for playing imperfect information games that are too large to be optimally solved. Although PIMC search has ...
Jeffrey Richard Long, Nathan R. Sturtevant, Michae...
AAAI
2010
15 years 8 months ago
To Max or Not to Max: Online Learning for Speeding Up Optimal Planning
It is well known that there cannot be a single "best" heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. b...
Carmel Domshlak, Erez Karpas, Shaul Markovitch
AAAI
2006
15 years 8 months ago
Bayesian Calibration for Monte Carlo Localization
Localization is a fundamental challenge for autonomous robotics. Although accurate and efficient techniques now exist for solving this problem, they require explicit probabilistic...
Armita Kaboli, Michael H. Bowling, Petr Musí...
AAAI
2006
15 years 8 months ago
Detecting Disjoint Inconsistent Subformulas for Computing Lower Bounds for Max-SAT
Many lower bound computation methods for branch and bound Max-SAT solvers can be explained as procedures that search for disjoint inconsistent subformulas in the Max-SAT instance ...
Chu Min Li, Felip Manyà, Jordi Planes
AAAI
2006
15 years 8 months ago
Properties of Forward Pruning in Game-Tree Search
Forward pruning, or selectively searching a subset of moves, is now commonly used in game-playing programs to reduce the number of nodes searched with manageable risk. Forward pru...
Yew Jin Lim, Wee Sun Lee