The symbolic approach to machine learning has developed algorithms for learning First Order Logic concept definitions. Nevertheless, most of them are limited because of their impossibility to cope with numeric features, typical of real-world data. In this paper, a method to face this problem is proposed. In particular, an extended version of the system… (More)
The emergent application area of Ambient Intelligence provides new opportunities in the field of social interaction, and new means to conceive and design novel interface solutions for accessing social technologies. The maturity of technologies such as motion tracking, gesture recognition, facial expression and emotion recognition facilitate natural… (More)
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— The objective of this work is to suggest criteria and guidelines that can be used to design inclusive technological solutions to support human activity sharing in the context of Ambient Intelligence. These guidelines derive from the analysis of previous works in related fields such as cognitive engineering, usability, inclusive design and accessibility.… (More)
This paper presents a new methodology for the incremental refinement of a knowledge base consisting of inductive rules. The novelty of the approach resides in the use of a body of deep knowledge for guiding the process of rule refinement, even in case this deep knowledge is too complex or not specific enough to deductively generate classification rules.… (More)
Radial Basis Function Networks axe universal function approximators which can be easely constructed from rule sets learned by a symbolic learner. This paper proposes the use of a more expressive concept description language, based on first order Ingics, and of a learning system (FLASH) working in such an environment , in order to incorporate the feature… (More)