Incremental version-space merging: a general framework for concept learning
Incremental version-space merging: a general framework for concept learning
Extending learning to multiple agents: issues and a model for multi-agent machine learning (MA-ML)
EWSL-91 Proceedings of the European working session on learning on Machine learning
Argument based machine learning
Artificial Intelligence
An argumentation-based framework for deliberation in multi-agent systems
ArgMAS'07 Proceedings of the 4th international conference on Argumentation in multi-agent systems
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards a Logical Model of Induction from Examples and Communication
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Computing dialectical trees efficiently in possibilistic defeasible logic programming
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
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How to achieve shared meaning is a significant issue when more than one intelligent agent is involved in the same domain. We define the task of concept convergence, by which intelligent agents can achieve a shared, agreed-upon meaning of a concept (restricted to empirical domains). For this purpose we present a framework that, integrating computational argumentation and inductive concept learning, allows a pair of agents to (1) learn a concept in an empirical domain, (2) argue about the concept's meaning, and (3) reach a shared agreed-upon concept definition. We apply this framework to marine sponges, a biological domain where the actual definitions of concepts such as orders, families and species are currently open to discussion. An experimental evaluation on marine sponges shows that concept convergence is achieved, within a reasonable number of interchanged arguments, and reaching short and accurate definitions (with respect to precision and recall).