This paper argues that multiagent learning is a potential “killer application” for generative and developmental systems (GDS) because key challenges in learning to coordinate ...
Abstract. Networked multi-agent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply cha...
We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All...
Embedded systems consisting of collaborating agents capable of interacting with their environment are becoming ubiquitous. It is crucial for these systems to be able to adapt to t...
As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increasingly compl...
John Reeder, Gita Sukthankar, Michael Georgiopoulo...