C4.5: programs for machine learning
C4.5: programs for machine learning
Methods for task allocation via agent coalition formation
Artificial Intelligence
Tree based discretization for continuous state space reinforcement learning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
RoboCup Rescue: A Grand Challenge for Multi-Agent Systems
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Autonomous Agents that Learn to Better Coordinate
Autonomous Agents and Multi-Agent Systems
The Dynamic Selection of Coordination Mechanisms
Autonomous Agents and Multi-Agent Systems
RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies
Autonomous Agents and Multi-Agent Systems
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Coordinating agents in a complex environment is a hard problem, but it can become even harder when certain characteristics of the tasks, like the required number of agents, are unknown. In those settings, agents not only have to coordinate themselves on the different tasks, but they also have to learn how many agents are required for each task. To achieve that, we have elaborated a selective perception reinforcement learning algorithm to enable agents to learn the required number of agents. Even though there were continuous variables in the task description, the agents were able to learn their expected reward according to the task description and the number of agents. The results, obtained in the RoboCupRescue, show an improvement in the agents overall performance.