A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction

This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization

From sequential to parallel Inductive Logic Programming

Inductive Logic Programming (ILP) has achieved considerablesuccess in a wide range of domains. It is recognized however thateciency is a major obstacle to the use of ILP systems in applicationsrequiring large amounts of data. In this paper we address the problem ofeciency in ILP in three steps: i) we survey speedup techniques proposedfor sequential execution of ILP systems; ii) we survey dierent ways ofparallelizing an ILP system and; ii) adapt and combine the sequentialexecution speedup techniques in the parallel implementations of an ILPsystem. We also propose a novel technique to partition the search spaceinto independent sub-spaces that may be adequately searched in parallel

Similarity measures over refinement graphs

Similarity also plays a crucial role in support vector machines. Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will define and analyze two similarity measures, S λ and S π, based on refinement graphs. The anti-unification-based similarity, S λ, assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The property-based similarity, S π, is based on a process of disintegrating the instances into a set of properties, and then analyzing these property sets. Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness. © 2011 The Author(s).Support for this work came from the project Next-CBR TIN2009-13692-C03-01 (co-sponsored by EU FEDER funds)Peer Reviewe

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Contributions to artificial intelligence: the IIIA perspective

La intel·ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel·ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel·ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel·ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel·ligència artificial de què estiguin dotats no tingui res a veure amb la intel·ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel·ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel·ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years