The Machine Learning group at the Dipartimento di Informatica, Università
di Torino is part of a larger research group on Artificial
Intelligence, and is active since 1984.
In 1998, due to the birth of the Università del Piemonte Orientale, part
of the group joined the new University and started a fresh research group there.
Components of the group are:
At Università di Torino:
On average, four undergraduate students per year join the group.
At Università del Piemonte Orientale:
The research in ML has been focussed, at the beginning, on the
development of a symbolic concept learning system, ML-SMART, able to
acquire a network of first-order logic classification rules in noisy
domains. The system, initially based on inductive techniques, has been
extended, later, with a deductive component and, more recently, also by
including abductive reasoning, the ability of learning intensional
definitions of relations and of handling continuous-valued numerical
features (SMART+). With an incremental version of this system, ENIGMA,
an industrial diagnostic expert system has been learned and used
successfully in field. Another system, RIGEL, also devoted to the
acquisition of first-order concept descriptions, has been developed
along the same research line.
New directions in symbolic ML have been explored. On one hand, an
abductive reasoning mechanism, using deep models of the domain, has
been implemented in a new system WHY. This system served also as a tool
to explore the possibility of speeding up learning by suitably
selecting the examples, according to the deep model, and their
presentation order. On the other hand, issues in knowledge
representation for ML, such as exploitation of abstraction theories,
have been proposed. More recently, the research themes have been
extended to also include Genetic Algorithms and Neural Networks.
In particular, the system REGAL, able to learn first-order logic concept
descriptions using a parallel genetic search, has been designed and
implemented. A new selection operator has been proposed and a
theoretical analysis of its behaviour has been performed.
On the neural network side, the focus of attention is about locally
receptive fields networks (Radial Basis Functions and Fuzzy Logic
Controllers), in particular about how symbolic knowledge, generated with
classical ML approaches, can be used to initialize a network, and
how a network can be refined by means of Reinforcement Learning.
Finally, also some work has been done in COLT, in learning recursive
concept definitions, and in semi-automated knowledge elicitation
The target of another currently in progress research project, consists
in using Mobile Agents for automatic information retrieval, network management
and security purposes within a distributed system. A fundamental aspect of
this architecture is the capability of using ILP methods in order to
improve the agents functionalities.
The group receives funds from the Italian Education and Research
Departments, from the National Research Council (CNR) and from several
cooperations with industries. Moreover, the group has been involved in
ESPRIT Projects, namely on Special Algorithms and Architectures for
Speech and Image Processing, and on Learning in Robotics.