Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Explanation component of software system
Crossroads - Special issue on object oriented programming
Automatically Selecting Strategies for Multi-Case-Base Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Retrieval and Reasoning in Distributed Case Bases
Retrieval and Reasoning in Distributed Case Bases
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
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Incorporating a Temporal Bounded Execution to the CBR Methodology
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
The Knowledge Engineering Review
Partners selection in multi-agent systems by using linear and non-linear approaches
Transactions on computational science I
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Expert Systems with Applications: An International Journal
A case-based approach to open-ended collective agreement with rational ignorance
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
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ACM Transactions on Intelligent Systems and Technology (TIST)
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Artificial Intelligence
Argue to agree: A case-based argumentation approach
International Journal of Approximate Reasoning
Case-based strategies for argumentation dialogues in agent societies
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
ArgCBROnto: a knowledge representation formalism for case-based argumentation
AT'13 Proceedings of the Second international conference on Agreement Technologies
Multiagent and Grid Systems
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In this paper we will present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication. The AMAL framework is completely based on learning from examples: the argument preference relation, the argument generation policy, and the counterargument generation policy are case-based techniques. For join deliberation, learning agents share their experience by forming a committee to decide upon some joint decision. We experimentally show that the argumentation among committees of agents improves both the individual and joint performance. For learning from communication, an agent engages into arguing with other agents in order to contrast its individual hypotheses and receive counterexamples; the argumentation process improves their learning scope and individual performance.