The problem of replicating the flexibility of human common-sense reasoning has captured the imagination of computer scientists since the early days of Alan Turing's foundational work on computation and the philosophy of artificial intelligence. In the intervening years, the idea of cognition as computation has emerged as a fundamental tenet of Artificial Intelligence (AI) and cognitive science. But what kind of computation is cognition? We describe a computational formalism centered around a probabilistic Turing machine called QUERY, which captures the operation of probabilistic conditioning via conditional simulation. Through several examples and analyses, we demonstrate how the QUERY abstraction can be used to cast common-sense reasoning as probabilistic inference in a statistical model of our observations and the uncertain structure of the world that generated that experience. This formulation is a recent synthesis of several research programs in AI and cognitive science, but it also represents a surprising convergence of several of Turing's pioneering insights in AI, the foundations of computation, and statistics.

This book chapter is an introduction to and an overview of the information-theoretic, task independent utility function "Empowerment", which is defined as the channel capacity between an agent's actions and an agent's sensors. It quantifies how much influence and control an agent has over the world it can perceive. This book chapter discusses the general idea behind empowerment as an intrinsic motivation and showcases several previous applications of empowerment to demonstrate how empowerment can be applied to different sensor-motor configuration, and how the same formalism can lead to different observed behaviors. Furthermore, we also present a fast approximation for empowerment in the continuous domain.

We present the concept of Semantic Advertising which we see as the future of online advertising. Semantic Advertising is online advertising powered by semantic technology which essentially enables us to represent and reason with concepts and the meaning of things. This paper aims to 1) Define semantic advertising, 2) Place it in the context of broader and more widely used concepts such as the Semantic Web and Semantic Search, 3) Provide a survey of work in related areas such as context matching, and 4) Provide a perspective on successful emerging technologies and areas of future work. We base our work on our experience as a company developing semantic technologies aimed at realizing the full potential of online advertising.

Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. Although various routing protocols, incorporating varying degrees of adaptiveness, have been proposed for CRNs, it is imperative for the long term success of CRNs that the design of cognitive routing protocols be pursued by the research community. Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design. In this paper, we provide a self-contained tutorial on various AI and machine-learning techniques that have been, or can be, used for developing cognitive routing protocols. We also survey the application of various classes of AI techniques to CRNs in general, and to the problem of routing in particular. We discuss various decision making techniques and learning techniques from AI and document their current and potential applications to the problem of routing in CRNs. We also highlight the various inference, reasoning, modeling, and learning sub tasks that a cognitive routing protocol must solve. Finally, open research issues and future directions of work are identified.

Artifact-Centric systems have emerged in the last years as a suitable framework to model business-relevant entities, by combining their static and dynamic aspects. In particular, the Guard-Stage-Milestone (GSM) approach has been recently proposed to model artifacts and their lifecycle in a declarative way. In this paper, we enhance GSM with a Semantic Layer, constituted by a full-fledged OWL 2 QL ontology linked to the artifact information models through mapping specifications. The ontology provides a conceptual view of the domain under study, and allows one to understand the evolution of the artifact system at a higher level of abstraction. In this setting, we present a technique to specify temporal properties expressed over the Semantic Layer, and verify them according to the evolution in the underlying GSM model. This technique has been implemented in a tool that exploits state-of-the-art ontology-based data access technologies to manipulate the temporal properties according to the ontology and the mappings, and that relies on the GSMC model checker for verification.

The Classification Literature Automated Search Service, an annual bibliography based on citation of one or more of a set of around 80 book or journal publications, ran from 1972 to 2012. We analyze here the years 1994 to 2011. The Classification Society's Service, as it was termed, has been produced by the Classification Society. In earlier decades it was distributed as a diskette or CD with the Journal of Classification. Among our findings are the following: an enormous increase in scholarly production post approximately 2000; a very major increase in quantity, coupled with work in different disciplines, from approximately 2004; and a major shift also from cluster analysis in earlier times having mathematics and psychology as disciplines of the journals published in, and affiliations of authors, contrasted with, in more recent times, a "centre of gravity" in management and engineering.

Throughout the history of games, representing the abilities of the various agents acting on behalf of the players has been a central concern. With increasingly sophisticated games emerging, these simulations have become more realistic, but the underlying mechanisms are still, to a large extent, of an ad hoc nature. This paper proposes using a logistic model from psychometrics as a unified mechanism for task resolution in simulation-oriented games.

The crucial importance of metrics in machine learning algorithms has led to an increasing interest in optimizing distance and similarity functions, an area of research known as metric learning. When data consist of feature vectors, a large body of work has focused on learning a Mahalanobis distance. Less work has been devoted to metric learning from structured objects (such as strings or trees), most of it focusing on optimizing a notion of edit distance. We identify two important limitations of current metric learning approaches. First, they allow to improve the performance of local algorithms such as k-nearest neighbors, but metric learning for global algorithms (such as linear classifiers) has not been studied so far. Second, the question of the generalization ability of metric learning methods has been largely ignored. In this thesis, we propose theoretical and algorithmic contributions that address these limitations. Our first contribution is the derivation of a new kernel function built from learned edit probabilities. Our second contribution is a novel framework for learning string and tree edit similarities inspired by the recent theory of (e,g,t)-good similarity functions. Using uniform stability arguments, we establish theoretical guarantees for the learned similarity that give a bound on the generalization error of a linear classifier built from that similarity. In our third contribution, we extend these ideas to metric learning from feature vectors by proposing a bilinear similarity learning method that efficiently optimizes the (e,g,t)-goodness. Generalization guarantees are derived for our approach, highlighting that our method minimizes a tighter bound on the generalization error of the classifier. Our last contribution is a framework for establishing generalization bounds for a large class of existing metric learning algorithms based on a notion of algorithmic robustness.

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid and other nonlinear models need to be considered, for instance, when there are multi-scattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this paper, we present an overview of recent advances in nonlinear unmixing modeling.

The digitalization of music and the instant availability of millions of tracks on the Internet require new approaches to support the user in the exploration of these huge music collections. One possible approach to address this problem, which can also be found on popular online music platforms, is the use of user-created or automatically generated playlists (mixes). The automated generation of such playlists represents a particular type of the music recommendation problem with two special characteristics. First, the tracks of the list are usually consumed immediately at recommendation time; secondly, songs are listened to mostly in consecutive order so that the sequence of the recommended tracks can be relevant. In the past years, a number of different approaches for playlist generation have been proposed in the literature. In this paper, we review the existing core approaches to playlist generation, discuss aspects of appropriate offline evaluation designs and report the results of a comparative evaluation based on different datasets. Based on the insights from these experiments, we propose a comparably simple and computationally tractable new baseline algorithm for future comparisons, which is based on track popularity and artist information and is competitive with more sophisticated techniques in our evaluation settings.