The weighted majority algorithm
Information and Computation
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An Behavior-based Robotics
Learning to weigh basic behaviors in scalable agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multi-criteria Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multiagent coordination by Extended Markov Tracking
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Towards flexible teamwork in behavior-based robots: extended abstract
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An argumentation based approach for practical reasoning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Towards collaborative task and team maintenance
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dynamics based control with an application to area-sweeping problems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
The value of observation for monitoring dynamic systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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We introduce a multi-model variant of the EMT-based control algorithm. The new algorithm, MM-EMT, is capable of balancing several control tasks expressed using separate dynamic models with a common action space. Such multiple models are common in both single-agent environments, when the agent has multiple tasks to achieve, and in team activities, when agent actions affect both the local agent's task as well as the overall team's coordination. To demonstrate the behaviour that MM-EMT engenders, several experimental setups were devised. Simulation results support the effectiveness of the approach, which in the multi-agent scenario is expressed in the MM-EMT algorithm's ability to balance local and team-coordinated motion requirements.