Local strategy learning in networked multi-agent team formation Export

@article{citeulike:1693765, abstract = {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 chain networks and sensor networks. A common challenge in many networked multi-agent systems is decentralized team formation among the spatially and logically extended agents. Even in cooperative multi-agent systems, efficient team formation is made difficult by the limited local information available to the individual agents. We present a model of distributed multi-agent team formation in networked multi-agent systems, describe a policy learning framework for joining teams based on local information, and give empirical results on improving team formation performance. In particular, we show that local policy learning from limited information leads to a significant increase in organizational team formation performance compared to a random policy.}, author = {Bulka, Blazej and Gaston, Matthew and Desjardins, Marie}, citeulike-article-id = {1693765}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10458-006-0007-x}, day = {24}, doi = {10.1007/s10458-006-0007-x}, journal = {Autonomous Agents and Multi-Agent Systems}, keywords = {learning, multiagent}, number = {1}, pages = {29--45}, posted-at = {2007-09-28 19:33:46}, priority = {2}, title = {Local strategy learning in networked multi-agent team formation}, url = {http://dx.doi.org/10.1007/s10458-006-0007-x}, volume = {15}, year = {2007} }

TY - JOUR ID - citeulike:1693765 L3 - citeulike-article-id:1693765 N2 - 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 chain networks and sensor networks. A common challenge in many networked multi-agent systems is decentralized team formation among the spatially and logically extended agents. Even in cooperative multi-agent systems, efficient team formation is made difficult by the limited local information available to the individual agents. We present a model of distributed multi-agent team formation in networked multi-agent systems, describe a policy learning framework for joining teams based on local information, and give empirical results on improving team formation performance. In particular, we show that local policy learning from limited information leads to a significant increase in organizational team formation performance compared to a random policy. IS - 1 JF - Autonomous Agents and Multi-Agent Systems EP - 45 TI - Local strategy learning in networked multi-agent team formation VL - 15 SP - 29 KW - learning KW - multiagent AU - Bulka, Blazej AU - Gaston, Matthew AU - Desjardins, Marie PY - 2007//24/ UR - http://dx.doi.org/10.1007/s10458-006-0007-x ER -