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90
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MA
1999
Springer
87views Communications» more  MA 1999»
15 years 5 months ago
Communicating Neural Network Knowledge between Agents in a Simulated Aerial Reconnaissance System
In order to maintain their performance in a dynamic environment, agents may be required to modify their learning behavior during run-time. If an agent utilizes a rule-based system...
Stephen Quirolgico, K. Canfield, Timothy W. Finin,...
123
Voted
AAMAS
2002
Springer
15 years 18 days ago
Relational Reinforcement Learning for Agents in Worlds with Objects
In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it ...
Saso Dzeroski
118
Voted
AAAI
2011
14 years 22 days ago
CCRank: Parallel Learning to Rank with Cooperative Coevolution
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coev...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
ATAL
2008
Springer
15 years 2 months ago
Norm emergence under constrained interactions in diverse societies
Effective norms, emerging from sustained individual interactions over time, can complement societal rules and significantly enhance performance of individual agents and agent soci...
Partha Mukherjee, Sandip Sen, Stéphane Airi...
ICML
1994
IEEE
15 years 4 months ago
Markov Games as a Framework for Multi-Agent Reinforcement Learning
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function....
Michael L. Littman