In this paper we describe an integrated multilevel learning approach to multiagent coalition formation in a real-time environment. In our domain, agents negotiate to form teams to...
In this paper, we describe how certain aspects of the biological phenomena of stigmergy can be imported into multiagent reinforcement learning (MARL), with the purpose of better e...
There is increasing research interest in solving routing problems in sensor networks subject to constraints such as data correlation, link reliability and energy conservation. Sin...
Petroleum industry production systems are highly automatized. In this industry, all functions (e.g., planning, scheduling and maintenance) are automated and in order to remain comp...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...