Although well understood in the single-agent framework, the use of traditional reinforcement learning (RL) algorithms in multi-agent systems (MAS) is not always justified. The fe...
Distributed problem solving by a multiagent system represents a promising approach to solving complex computational problems. However, many multiagent systems require certain degr...
The effectiveness of simulation-based training for individual tasks – such as piloting skills – is well established, but its use for team training raises challenging technical...
David R. Traum, Jeff Rickel, Jonathan Gratch, Stac...
This paper introduces a methodology to help the programmer in the transition from a set of desired global properties expressed as an equation-based model (EBM) that a Multi-Agent ...
In complex distributed applications, a problem is often decomposed into a set of subproblems that are distributed to multiple agents. We formulate this class of problems with a tw...
Abstract. A self-organising system functions without central control, and through contextual local interactions. Components achieve a simple task individually, but a complex collec...
Giovanna Di Marzo Serugendo, Noria Foukia, Salima ...
Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such ...