TY - JOUR
T1 - Disturbed bayesian learning in multiagent systems
T2 - Improving our understanding of its capabilities and limitations
AU - Djurić, Petar M.
AU - Wang, Yunlong
PY - 2012/3
Y1 - 2012/3
N2 - In this article, we study social networks of agents, where agents learn not only from private signals (i.e., signals only available to the agents receiving them), but from other agents too. Based on all the available information, agents modify their beliefs in events of interest and make decisions on which actions to take based on the beliefs. In doing so, they optimize functions that reflect some (cumulative) reward. This problem has been studied in various disciplines including control theory, operations research, artificial intelligence, game theory, information theory, economics, statistics, computer science, and signal processing.
AB - In this article, we study social networks of agents, where agents learn not only from private signals (i.e., signals only available to the agents receiving them), but from other agents too. Based on all the available information, agents modify their beliefs in events of interest and make decisions on which actions to take based on the beliefs. In doing so, they optimize functions that reflect some (cumulative) reward. This problem has been studied in various disciplines including control theory, operations research, artificial intelligence, game theory, information theory, economics, statistics, computer science, and signal processing.
UR - http://www.scopus.com/inward/record.url?scp=84863127298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863127298&partnerID=8YFLogxK
U2 - 10.1109/MSP.2011.943495
DO - 10.1109/MSP.2011.943495
M3 - Article
SN - 1053-5888
VL - 29
SP - 65
EP - 76
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 2
M1 - 6153148
ER -