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Teaching case-based argumentation through a model and examples
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This paper describes an approach to multi-agent classification using an argumentation from experience paradigm whereby individual agents argue for a given example to be classified with a particular label according to their local data. Arguments are expressed in the form of classification rules which are generated dynamically. As such each local database can be conceptualised as an experience repository; and the individual classification rules, generated from this repository, as describing generalisations drawn from this experience. The argumentation process and the supporting data structures are fully described. The process has been implemented in the PISA (Pooling Information from Several Agents) multi-agent framework which is fully described. Experiments indicate that the operation of PISA is comparable with other classification approaches and that, when operating groups or in the presence of noise, PISA outperforms such comparable approaches.