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» Fast Planning in Stochastic Games
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SOFSEM
2007
Springer
13 years 11 months ago
Incremental Learning of Planning Operators in Stochastic Domains
In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
Javad Safaei, Gholamreza Ghassem-Sani
HICSS
2006
IEEE
117views Biometrics» more  HICSS 2006»
13 years 11 months ago
Planning for a Big Bang in a Supply Chain: Fast Hedging for Production Indicators
— We concern ourselves with the process of making optimized production planning decisions in the face of low frequency, high impact uncertainty, which takes the form of a small n...
David L. Woodruff, Stefan Voß
AIPS
2000
13 years 6 months ago
DPPlan: An Algorithm for Fast Solutions Extraction from a Planning Graph
Themostefficient planning algorithms recently developed are mainly based on Graphplansystem or on satisfiability approach. In this paper wepresent a new approach to plan generatio...
Marco Baioletti, Stefano Marcugini, Alfredo Milani
ICML
2003
IEEE
14 years 5 months ago
BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games
We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number...
Vincent Conitzer, Tuomas Sandholm
FLAIRS
2009
13 years 2 months ago
Dynamic Programming Approximations for Partially Observable Stochastic Games
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...
Akshat Kumar, Shlomo Zilberstein