Multiagent Inductive Learning: an Argumentation-based Approach

Multiagent Inductive Learning is the problem that groups of agents face when they want to perform inductive learning, but the data of interest is distributed among them. This paper focuses on concept learning, and presents A-MAIL, a framework for multiagent induction integrating ideas from inductive learning, case-based reasoning and argumentation. Argumentation is used as a communication framework with which the agents can communicate their inductive inferences to reach shared and agreed-upon concept definitions. We also identify the requirements for learning algorithms to be used in our framework, and propose an algorithm which satisfies them.