Collaborative multiagent learning for classification tasks

Multiagent learning di ers 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 distributed classi cation tasks, i.e. classi cation 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 classi cation task, show that existing classi cation algorithms are not satisfactory for distributed classi cation tasks, and nally, to show that our collaborative learning algorithm performs well at distributed classi cation.

• Type: Conference
• Year: 2001
• Where: AGENTS
• Authors: Pragnesh Jay Modi, Wei-Min Shen
• Keywords: AGENTS 2001, Classi Cation, Classi Cation Algorithms, Classi Cation Tasks, Security Privacy