ECAI 2004 Conference Paper

[full paper]

Automatic Induction of Domain-related Information: Learning Descriptors Type Domains

Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, Nicola Di Mauro

Learning in complex contexts often requires pure induction to be supported by various kinds of meta-information on the domain itself and/or on its representation. Providing such information is a critical issue for the learning task, and is often in charge of the human expert. It is also a difficult and error-prone activity, in which mistakes are highly probable because of a number of factors. This makes it desirable to develop procedures that can automatically generate such information starting from the same observations that are input to the learning process. This paper focuses on a specific kind of meta-information: the types used in the description language and their related domains. Indeed, many learning systems known in the literature are able to exploit (and sometimes require) such a kind of knowledge to improve their performance. An algorithm is proposed to automatically identify types from observations, and detailed examples of its behaviour are given. An evaluation of its performance in domains with different characteristics is reported, and its robustness with respect to incomplete observations is studied.

Keywords: Logic Programming, Knowledge Representation, Machine Learning

Citation: Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, Nicola Di Mauro: Automatic Induction of Domain-related Information: Learning Descriptors Type Domains. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.1011-1012.