Doubts and Equilibria

In real life strategic interactions, decision-makers are likely to entertain doubts about the degree of optimality of their play. To capture this feature of real choice-making, we present here a model based on the doubts felt by an agent about how well is playing a game. The doubts are coupled with (and mutually reinforced by) imperfect discrimination capacity, which we model here by means of similarity relations. We assume that each agent builds procedural preferences de ned on the space of expected payoffs-strategy frequencies attached to his current strategy. These preferences, together with an adaptive learning process lead to doubt-based selection dynamic systems. We introduce the concepts of Mixed Strategy Doubt Equilibria, Mixed Strategy Doubt-Full Equilibria and Mixed Strategy Doubtless Equilibria and show the theoretical and the empirical relevance of these concepts.

Learning from human teaching strategies

Recent works in the area of adaptive education systems point out the importance of aumenting the student model to improve the personalization and adaptation to the learner by means of several aspects such as emotions, user locations or interactions. Until now the study of interactions has been mainly focused on the student-learning system flow, despite the fact that the most successful and used way of teaching are the traditional face-to-face interactions. In this project, we explore the use of interactions among teachers and students, as they occur in traditional education, to enrich the current student models, with the aim of providing them with useful information about new characteristics for improving the learning process. At a first step, in this paper we present the formal process carried out to obtain information about teachers’ expertise and necessities regarding the direct interactions with students.

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.

Adaptive Scalable SVD Unit for Fast Processing of Large LSE Problems

Singular Value Decomposition (SVD) is a key linear algebraic operation in many scientific and engineering applications. In particular, many computational intelligence systems rely on machine learning methods involving high dimensionality datasets that have to be fast processed for real-time adaptability. In this paper we describe a practical FPGA (Field Programmable Gate Array) implementation of a SVD processor for accelerating the solution of large LSE problems. The design approach has been comprehensive, from the algorithmic refinement to the numerical analysis to the customization for an efficient hardware realization. The processing scheme rests on an adaptive vector rotation evaluator for error regularization that enhances convergence speed with no penalty on the solution accuracy. The proposed architecture, which follows a data transfer scheme, is scalable and based on the interconnection of simple rotations units, which allows for a trade-off between occupied area and processing acceleration in the final implementation. This permits the SVD processor to be implemented both on low-cost and highend FPGAs, according to the final application requirements.