Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
ion in model checking multi-agent systems Mika Cohen Department of Computing Imperial College London London, UK Mads Dam Access Linnaeus Center Royal Institute of Technology Stockh...
Mika Cohen, Mads Dam, Alessio Lomuscio, Francesco ...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems in multi-agent systems. Several current algorithms that solve general DCOP instan...
Performance analysis tools are critical for the effective use of large parallel computing resources, but existing tools have failed to address three problems that limit their scal...