C4.5: programs for machine learning
C4.5: programs for machine learning
Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
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
Introduction to Multiagent Systems
Introduction to Multiagent Systems
TTree: Tree-Based State Generalization with Temporally Abstract Actions
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Self-organization through bottom-up coalition formation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
Coalition calculation in a dynamic agent environment
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bayesian Reinforcement Learning for Coalition Formation under Uncertainty
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Coalition Formation: From Software Agents to Robots
Journal of Intelligent and Robotic Systems
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
An overview of combinatorial auctions
ACM SIGecom Exchanges
Multi-agent systems by incremental gradient reinforcement learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Methods for task allocation via agent coalition formation
Artificial Intelligence
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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 these 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 contribute to this problem, we present in this paper a selective perception reinforcement learning algorithm which enables agents to learn the required number of agents that should coordinate their efforts on a given task. Even though there are continuous variables in the task description, agents in our algorithm are able to learn their expected reward according to the task description and the number of agents. The results, obtained in the RoboCupRescue simulation environment, show an improvement in the agents overall performance.