Auction methods have been successfully used for coordinating teams of robots in the multi-robot routing problem, a representative domain for multi-agent coordination. Solutions to...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, ...
Physical agents (such as wheeled vehicles, UAVs, hovercraft, etc.) with simple control systems are often sensitive to changes in their physical design and control parameters. As s...
Ryan Connaughton, Paul W. Schermerhorn, Matthias S...
In this paper we study the use of experts algorithms in a multiagent setting. In this paper we allow agents to use multiple experts and explore different experts algorithms that a...
When the same set of people interact frequently with one another, they grow to think more and more along the same lines, a phenomenon we call "collective cognitive convergenc...
H. Van Dyke Parunak, Theodore C. Belding, Rainer H...
The multiply sectioned Bayesian network (MSBN) framework is the most studied approach for distributed Bayesian Network inference in an MAS setting. This paper describes a new fram...
This paper presents the design and implementation of negotiation agents that negotiate with other entities for acquiring multiple resources. In our approach, agents utilize a time...
Solutions to complex tasks often require the cooperation of multiple robots, however, developing multi-robot policies can present many challenges. In this work, we introduce teach...
We study principled methods for incorporating user utility into the selection of sponsored search ads. We describe variations of the GSP allocation/pricing mechanism that accommod...
We investigate equilibrium strategies for bidding agents that participate in multiple, simultaneous second-price auctions with perfect substitutes. For this setting, previous rese...
Enrico H. Gerding, Zinovi Rabinovich, Andrew Byde,...