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» Learning action effects in partially observable domains
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JAIR
2007
79views more  JAIR 2007»
14 years 9 months ago
Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting
We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-F...
Carmel Domshlak, Jörg Hoffmann
AIPS
2008
14 years 12 months ago
Stochastic Enforced Hill-Climbing
Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called "basin flooding"). ...
Jia-Hong Wu, Rajesh Kalyanam, Robert Givan
AAMAS
2007
Springer
14 years 9 months ago
A framework for meta-level control in multi-agent systems
Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and dom...
Anita Raja, Victor R. Lesser
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AAMAS
2006
Springer
14 years 9 months ago
Handling Communication Restrictions and Team Formation in Congestion Games
Abstract. There are many domains in which a multi-agent system needs to maximize a "system utility" function which rates the performance of the entire system, while subje...
Adrian K. Agogino, Kagan Tumer
IJCAI
1997
14 years 11 months ago
Change, Change, Change: Three Approaches
We consider the frame problem, that is, char­ acterizing the assumption that properties tend to persist over time. We show that there are at least three distinct assumptions that...
Tom Costello