50 search results - page 2 / 10 » Approximate state estimation in multiagent settings with con... |
ATVA
2006
Springer
13 years 11 months ago
2006
Springer
We address the problem of model checking hybrid systems which exhibit nontrivial discrete behavior and thus cannot be treated by considering the discrete states one by one, as most...
ICCBR
2005
Springer
14 years 1 months ago
2005
Springer
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
AIIDE
2006
13 years 9 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
AAMAS
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. There exist several convergent and consistent RL algorithms which have been intensivel...
AAMAS
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. We study optimal control in large stochastic multi-agent systems in continuous space and time. We consider multi-agent systems where agents have independent dynamics with...