Computer agents participate in many collaborative and competitive multiagent domains in which humans make decisions. For computer agents to interact successfully with people in su...
Many multiagent problems comprise subtasks which can be considered as reinforcement learning (RL) problems. In addition to classical temporal difference methods, evolutionary algo...
Jan Hendrik Metzen, Mark Edgington, Yohannes Kassa...
In this paper, we develop a novel algorithm that allows service consumer agents to automatically select and provision service provider agents for their workflows in highly dynamic...
Sebastian Stein, Nicholas R. Jennings, Terry R. Pa...
In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
Software maintenance and evolution is arguably a lengthy and expensive phase in the life cycle of a software system. A critical issue at this phase is change propagation: given a ...
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...
This paper studies price properties in continuous double-auction markets in the presence of marketmakers, agents with special responsibilities for maintaining liquidity and orderl...
This paper presents a theoretical study of decentralized control for sensing-based shape formation on modular multirobot systems, where the desired shape is specified in terms of ...
In this paper, we investigate multi-agent learning (MAL) in a multi-agent resource selection problem (MARS) in which a large group of agents are competing for common resources. Si...
Many practitioners view agent interaction protocols as rigid specifications that are defined a priori, and hard-code their agents with a set of protocols known at design time -- a...