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» Learning the required number of agents for complex tasks
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ICML
2008
IEEE
14 years 8 months ago
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Finale Doshi, Joelle Pineau, Nicholas Roy
24
Voted
ROBOCUP
2009
Springer
134views Robotics» more  ROBOCUP 2009»
14 years 2 months ago
Learning Complementary Multiagent Behaviors: A Case Study
As the reach of multiagent reinforcement learning extends to more and more complex tasks, it is likely that the diverse challenges posed by some of these tasks can only be address...
Shivaram Kalyanakrishnan, Peter Stone
ATAL
2006
Springer
13 years 11 months ago
Learning empathy: a data-driven framework for modeling empathetic companion agents
Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify the...
Scott W. McQuiggan, James C. Lester
TSMC
2008
229views more  TSMC 2008»
13 years 7 months ago
A Comprehensive Survey of Multiagent Reinforcement Learning
Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many task...
Lucian Busoniu, Robert Babuska, Bart De Schutter
AVSS
2005
IEEE
14 years 1 months ago
Integrating multi-camera tracking into a dynamic task allocation system for smart cameras
This paper reports on the integration of multi-camera tracking into an agent-based framework, which features autonomous task allocation for smart cameras targeting traffic survei...
Michael Bramberger, Markus Quaritsch, Thomas Winkl...