Coordinated inductive learning using argumentation-based communication

This paper focuses on coordinated inductive learning, concerning how agents with inductive learning capabilities can coordinate their learnt hypotheses with other agents. Coordination in this context means that the hypothesis learnt by one agent is consistent with the data known to the other agents. In order to address this problem, we present A-MAIL, an argumentation approach for agents to argue about hypotheses learnt by induction. A-MAIL integrates, in a single framework, the capabilities of learning from experience, communication, hypothesis revision and argumentation. Therefore, the A-MAIL approach is one step further in achieving autonomous agents with learning capabilities which can use, communicate and reason about the knowledge they learn from examples. © 2014, The Author(s).Research partially funded by the projects Next-CBR (TIN2009-13692-C03-01) and Cognitio (TIN2012-38450- C03-03) [both co-funded with FEDER], Agreement Technologies (CONSOLIDER CSD2007-0022), and by the Grants 2009-SGR-1433 and 2009-SGR-1434 of the Generalitat de Catalunya.Peer reviewe

Justification-based selection of training examples for case base reduction

Abstract. Maintaining compact and competent case bases has become a main topic of Case Based Reasoning (CBR) research. The main goal is to obtain a compact case base (with a reduced number of cases) without losing accuracy. In this work we present JUST, a technique to reduce the size of a case base while maintaining the classification accuracy of the CBR system. JUST uses justifications in order to select a subset of cases from the original case base that will form the new reduced case base. A justification is an explanation that the CBR system generates to justify the solution found for a given problem. Moreover, we present empirical evaluation in various data sets showing that JUST is an effective case base reduction technique that maintains the classification accuracy of the case base. Keywords: CBR, Case Base Management, Case Base Reduction.