Abstract
In this work, a proximity-based generic method for discovery of generalized knowledge is presented and implemented in the framework of a fuzzy logic programming language with a weak unification procedure that uses proximity relations to model uncertainty. This method makes use of the concept of λ-block characterizing the notion of equivalence when working with proximity relations. When the universe of discourse is composed of concepts which are related by proximity, the sets of λ-blocks extracted from that proximity relation can be seen as hierarchical sets of concepts grouped by abstraction level. Then, each group (forming a λ-block) can be labeled, with user help, by way of a more general descriptor in order to simulate a generalization process based on proximity. Thanks to this process, the system can learn concepts that were unknown initially and reply queries that it was not able to answer. The novelty of this work is that it is the first time a method, with analogous features to the one aforementioned, has been implemented inside a fuzzy logic programming framework. In order to check the feasibility of the method we have developed a software tool which have been integrated into the Bousi~Prolog system.
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Julián-Iranzo, P., Rubio-Manzano, C. (2013). A Proximity-Based Method for Discovery of Generalized Knowledge and Its Incorporation to the Bousi~Prolog System. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_27
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DOI: https://doi.org/10.1007/978-3-642-38682-4_27
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