R. Mooney. Integrating abduction and induction in machine learning. In Flach and Kakas [FK98]. Home/Search Document Details and Download
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Ai Miei Genitori Ii - Acknowledgements First Would (Correct)
....in [KR98] The work builds on earlier proposals in [DK96] and [ELM 96, LMMR97, LMMR98] for learning simpler forms of abductive theories. The use of abduction in learning, either in an implicit or explicit form, has recently been examined by several works [Abe98, AD94, AD95, Coh92, DRB92a, Moo98, IS95, KK98, Sak98] The abductive assumptions generated during learning are then used in different ways depending on the kind of learning task the system is performing. In this thesis, abduction is used explicitly as the basic covering relation for defining the concept learning problem. In many ....
....the concept learning problem. In many other cases, abduction is used as a useful mechanism that can support some of the activities of the learning system. For example, in theory revision, abduction is used as one of the basic revision operators for the overall learning process [AD94, DRB92a, Moo98, Sak98] For each individual positive example that is not entailed by the theory, abduction is applied to determine the set of assumptions that would allow it to be proved. These assumptions are then used to suggest where the current theory should be revised. In [DRB92a, AD94] the assumptions are ....
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R. Mooney. Integrating abduction and induction in machine learning. In Flach and Kakas [FK98].
A-System: Problem Solving through Abduction - Kakas, Van Nuffelen, Denecker (2001) (5 citations) (Correct)
.... et al. 1996] planning [Missiaen et al. 1995; Kakas et al. 2000; Shanahan, 2000] knowledge assimilation and belief revision, Inoue and Sakama, 1995; Pagnucco, 1996] multi agent coordination, Ciampolini et al. 2000; Kowalski and Sadri, 1999] and knowledge intensive learning [Muggleton, 2000; Mooney, 2000] The essential feature of this abductive approach to problem solving is the fact that it allows the application problems to be formalized directly in their high level declarative representation. A close link therefore emerges between declarative problem solving in AI and the logical reasoning ....
R.J. Mooney. Integrating abduction and induction in machine learning. In Abduction and Induction: essays on their relation and integration, pages 181--191. Kluwer Academic Press, 2000.
Theory Refinement of Bayesian Networks with Hidden Variables - Ramachandran (1998) (5 citations) (Correct)
....need additional antecedents. These observations not only diagnose the errors in the theory, they also suggest ways in which the theory could be revised to correct these errors. Most theory refinement systems use a combination of abduction and induction to effect such reasoning as described above (Mooney, 1997). For each positive example not provable by the theory, these systems use abduction to generate a small set of assumptions that would allow the example to be proved. These assumptions are then used to suggest modifications to the theory. For negative examples proved by the theory, these techniques ....
Mooney, R. J. (1997). Integrating abduction and induction in machine learning. In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI.
Theory Refinement for Bayesian Networks with Hidden Variables - Ramachandran, Mooney (1998) (5 citations) Self-citation (Mooney) (Correct)
....therefore techniques used to revise rule bases are useful. These methods attribute classification errors on particular examples to specific portions of the theory and directly construct revisions to handle the misclassified cases. Most logical refinement systems use abduction to diagnose faults (Mooney, 1997). Since Bayesian networks place no restrictions on the direction of inference, abduction can be performed using the standard inference algorithms. In addition, leak nodes (Pradhan et al. 1994) provide a way to model the incompleteness and incorrectness of a Bayesian network with noisy or and ....
Mooney, R. J. (1997). Integrating abduction and induction in machine learning. In Working Notes of the IJCAI97 Workshop on Abduction and Induction in AI, pp.
Abductive Concept Learning - Kakas, Riguzzi (1999) (2 citations) (Correct)
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) R. Mooney. Integrating abduction and induction in machine learning. In Flach and Kakas
Constructive Reinforcement Learning - Hernandez-Orallo (1999) (Correct)
No context found.
R.J. Mooney "Integrating Abduction and Induction in Machine Learning" in Peter Flach and Antonis Kakas (eds), Proceedings of the IJCAI'97 Workshop on Abduction and Induction in AI, Nagoya, Japan 1997.
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