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

My appreciation of Enric Trillas: A personal Note

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Towards artificial intelligence : advances, challenges, and risks

This text contains some reflections on artificial intelligence (AI). First, we distinguish between strong and weak AI, as well as the concepts related to general and specific AI. Following this, we briefly describe the main current AI models and discuss the need to provide common-sense knowledge to machines in order to advance towards the goal of a general AI. Next, we talk about the current trends in AI based on the analysis of large amounts of data, which has recently allowed experts to make spectacular progress. Finally, we discuss other topics which, now and in the future, will continue to be key in AI, before closing with a brief reflection on the risks of AI

Robust Bayesian Linear Classifier Ensembles

The original publication is available at http://www.springerlink.comEnsemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where incorrect modelling assumptions are possibly made, uniform averaging sometimes performs even better than bayesian model averaging. Linear mixtures over sets of models provide an space that includes uniform averaging as a particular case. We develop two algorithms for learning maximum a posteriori weights for linear mixtures, based on expectation maximization and on constrained optimization. We provide a nontrivial example of the utility of these two algorithms by applying them for one dependence estimators.We develop the conjugate distribution for one dependence estimators and empirically show that uniform averaging is clearly superior to BMA for this family of models. After that we empirically show that the maximum a posteriori linear mixture weights improve accuracy significantly over uniform aggregation.Peer reviewe

Playing with Cases: Rendering Expressive Music with Case-Based Reasoning

This article surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on case-based reasoning (CBR). Following a brief overview discussing why people prefer listening to expressive music instead of nonexpressive synthesized music, we examine a representative selection of well-known approaches to expressive computer,music performance with an emphasis on AI-related approaches. In the main part of the article we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on Tempo-Express, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting of complementing audio information with information about the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This article is based on the >2011 Robert S. Engelmore Memorial Lecture> given by the first author at AAAI/IAAI 2011.This research is partially supported by the Ministry of Science and Innovation of Spain under the project NEXT-CBR (TIN2009-13692-C03-01) and the Generalitat de Catalunya AGAUR Grant 2009-SGR-1434Peer Reviewe

Obstacle Detection and Alignment using an Stereo Camera Pair

An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most correspondence methods first extract descriptors of salient features from robot sensor data, then matches between features are searched and finally the transformation that relates the maps is estimated from such matches. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a bag of features approach to identify putative corresponding maps followed by a Gauss-Newton algorithm to find the transformation that relates both maps. The proposed method is evaluated on a typical office dataset showing good performance.This work has been partially funded by TIN 2006-15308-C02-02 project grant of the Ministry of Education of Spain

Comparing Combinations of Feature Regions for Panoramic VSLAM

Invariant (or covariant) image feature region detectors and descriptors are useful in visual robot navigation because they provide a fast and reliable way to extract relevant and discriminative information from an image and, at the same time, avoid the problems of changes in illumination or in point of view. Furthermore, complementary types of image features can be used simultaneously to extract even more information. However, this advantage always entails the cost of more processing time and sometimes, if not used wisely, the performance can be even worse. In this paper we present the results of a comparison between various combinations of region detectors and descriptors. The test performed consists in computing the essential matrix between panoramic images using correspondences established with these methods. Different combinations of region detectors and descriptors are evaluated and validated using ground truth data. The results will help us to find the best combination to use it in an autonomous robot navigation system.This work has been partially supported by the FI grant from the Generalitat de Catalunya, the European Social Fund and the MID-CBR project grant TIN2006-15140-C03-01 and FEDER funds.Peer reviewe

Evaluation of the SIFT Object Recognition Method in Mobile Robots

CCIA 2009. 12th International Conference of the Catalan Association for Artificial Intelligence, Cardona, Spain, 21-23 October, 2009General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects.This work was supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, and the MID-CBR project grant TIN2006-15140-C03-01 and FEDER funds and the grant 2005-SGR-00093 and the MIPRCV Consolider Imagennio 2010.Peer reviewe

Transferring knowledge as heuristics in reinforcement learning: A case-based approach

The goal of this paper is to propose and analyse a transfer learning meta-algorithm that allows the implementation of distinct methods using heuristics to accelerate a Reinforcement Learning procedure in one domain (the target) that are obtained from another (simpler) domain (the source domain). This meta-algorithm works in three stages: first, it uses a Reinforcement Learning step to learn a task on the source domain, storing the knowledge thus obtained in a case base; second, it does an unsupervised mapping of the source-domain actions to the target-domain actions; and, third, the case base obtained in the first stage is used as heuristics to speed up the learning process in the target domain. A set of empirical evaluations were conducted in two target domains: the 3D mountain car (using a learned case base from a 2D simulation) and stability learning for a humanoid robot in the Robocup 3D Soccer Simulator (that uses knowledge learned from the Acrobot domain). The results attest that our transfer learning algorithm outperforms recent heuristically-accelerated reinforcement learning and transfer learning algorithms. © 2015 Elsevier B.V.Luiz Celiberto Jr. and Reinaldo Bianchi acknowledge the support of FAPESP (grants 2012/14010-5 and 2011/19280-8). Paulo E. Santos acknowledges support from FAPESP (grant 2012/04089-3) and CNPq (grant PQ2 -303331/2011-9).Peer Reviewe