Machine Learning and Inductive Logic Programming for Multi-agent Systems
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Teams of intelligent agents which learn using artificial immune systems
Journal of Network and Computer Applications - Special issue: Innovations in agent collaboration
Learning and communication via imitation: an autonomous robot perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We investigate the problem of agent communication when such agents are cooperating rather than competing. We do this using the statistical technique of canonical correlation analysis. We consider two stochastic process methods for performing canonical correlation analysis (CCA). The first creates a Gaussian Process formulation of regression in which we use the current projection of one data set as the target for the other and then repeat in the opposite direction; this is useful in the very limited environment in which the agents have prior and precise knowledge of the nature of their interaction. We extend the problem by creating a problem which no single agent can solve, but for which together they can find a solution. We investigate methods by which we can automatically find the number and composition of the groups necessary to solve the problem. Finally we develop a Dirichlet process of Gaussian models in which the Gaussian models are determined by Probabilistic CCA [2]. The Dirichlet Process enables us to have groups of agents cooperating on a task without having to specify in advance how many groups of agents there are or how many agents join each group.