Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Machine Learning
Argument based machine learning applied to law
Artificial Intelligence and Law - Argumentation in artificial intelligence and law
Argument based machine learning in a medical domain
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Why is rule learning optimistic and how to correct it
ECML'06 Proceedings of the 17th European conference on Machine Learning
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In this paper, some recent ideas will be presented about making machine learning (ML) more effective through mechanisms of argumentation. In this sense, argument-based machine learning (ABML) is defined as a refinement of the usual definition of ML. In ABML, some learning examples are accompanied by arguments, that are expert's reasons for believing why these examples are as they are. Thus ABML provides a natural way of introducing domain-specific prior knowledge in a way that is different from the traditional, general background knowledge. The task of ABML is to find a theory that explains the “argumented” examples by making reference to the given reasons. ABML, so defined, is motivated by the following advantages in comparison with standard learning from examples: (1) arguments impose constraints over the space of possible hypotheses, thus reducing search complexity, and (2) induced theories should make more sense to the expert. Ways of realising ABML by extending some existing ML techniques are discussed, and the aforementioned advantages of ABML are demonstrated experimentally.