In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. E ective agent interactions in such domains raise some of most fundamental research...
Massive amounts of raw data are currently being generated by biologists while sequencing organisms. Outside of the largest, high-pro le projects such as the Human Genome Project, ...
Research in multi-agent systems has led to the development of many multi-agent control architectures. However, we believe that there is currently no known optimal structure for mu...
Thuc Vu, Jared Go, Gal A. Kaminka, Manuela M. Velo...
This paper presents DARX, our framework for building applications that provide adaptive fault tolerance. It relies on the fact that multi-agent platforms constitute a very strong ...
d to submit first an abstract (to signal your interest and contribution) and then a full paper (for review) to the workshop. All accepted papers will be published as a volume from ...
Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement l...
In this paper we present the Thistle multi-agent system Help Desk application for helping an end user solve network interoperability problems on their own.1
Joseph A. Giampapa, Katia Sycara-Cyranski, Austin ...
s for Multi-Agent Only Knowing (extended abstract) Arild Waaler1,2 and Bjørnar Solhaug3,4 1 Finnmark College, Norway 2 Dep. of Informatics, University of Oslo, Norway 3 SINTEF ICT...
There is a growing need for a theory of “local to global” in distributed multi-agent systems, one which is able systematically to describe and analyze a variety of problems. T...