Towards Argumentation-based Multiagent Induction

Argumentation-based Example Interchage for Multiagent Induction

Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation-based framework for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in whi...

Empirical Argumentation: Integrating Induction and Argumentation in MAS

This paper presents an approach that integrates notions and techniques from two distinct fields of study —namely inductive learning and argumentation in multiagent systems (MAS). We will first discuss inductive learning and the role argumentation plays in multiagent inductive learning. Then we focus on how inductive learning can be used to realize argumentation in MAS based on empirical grounds...

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 frame...

Argumentation-based Example Interchange for Multiagent Induction

A defeasible reasoning model of inductive concept learning from examples and communication

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they co...