The

Team

Presentation:

• Machine Learning and Data Mining
• Knowledge Discovery
• Multi-paradigm Inductive Programming
• ROC analysis, cost-sensitive learning and model evaluation for decision support
• Agreement Technologies.
• Agent Intelligence Evaluation.
• MML induction and Solomonoff prediction.
• Probabilistic (inductive) programming.
• Inductive Debugging.
• Universal Psychometrics.

For a more comprehensive account of the team's activities and projects, you can take a look a this presentation (as for 2009) .

Learning Systems and other software:

• The FLIP system (1998-2001, click here for downloading the software and for more information): implements a framework for the Induction of Functional Logic Programs (IFLP) from facts. This can be seen as an extension to the now consolidated field of Inductive Logic Programming (ILP). Inspired in the inverse resolution operator of ILP, the system is based on the reversal of narrowing, the more usual operational mechanism for Functional Logic Programming. The main advantages of the FLIP system over the most used ILP systems are a natural handling of functions, without the use of mode or determinism declarations, and its power for inducing short recursive programs. Its applications are mainly program synthesis, program debugging and data mining of small highly structured documents.

• The SMILES system (2001-2002, click here for downloading the software and for more information): a machine learning system that integrates many different features from other machine learning techniques and paradigms and, more importantly, it presents several innovations in almost all of these features. In particular, SMILES extends classical decision tree learners in many ways (new splitting criteria, non-greedy search, new partitions, extraction of several and different solutions), it has an anytime handling of resources, and has a sophisticated and quite effective handling of costs. In this way, SMILES combines and improves the recent interest in hypotheses combination (e.g. boosting) and cost-sensitive learning (a priori and a posteriori class assignments, ROC analysis) outperforming previous systems in many situations. Its applications are basically data-mining and any other machine learning task where decision trees could be useful.

• The DBDT system (2004-2010, click here for downloading the software and for more information): is a machine learning algorithm that integrates decision tree learning and center splitting. Roughly speaking, the inferred classifer can be viewed as a tree of attribute prototypes (The value distribution of an attribute is represented by a set of prototypes.). An instance is linked to one prototype or other depending on its proximity.

• A machine intelligence testing framework system (2010-2012), click here for downloading the software. For more information, see the project Anytime Universal Intelligence .