Collaborative multiagent learning for classification tasks
Pragnesh Jay Modi, Wei‐Min Shen · 2001
Multiagent learning differs from standard machine learning in that most existing learning methods assume that all knowledge is available locally in a single agent. In multiagent systems, this assumption does not hold because relevant knowledge is distributed among the agents within the system. We describe a decentralized learning algorithm for {\it distributed classification tasks}, i.e. classification when the attributes are distributed among a set of agents and cannot be gathered into a central agent. Our main contribution is to introduce and formalize the distributed classfication task, show that existing classification algorithms are not satisfactory for distributed classification tasks, and finally, to show that our collaborative learning algorithm performs well at distributed classification.