When Kurt Goedel layed the foundations of theoretical computer science in 1931, he also introduced essential concepts of the theory of Artificial Intelligence (AI). Although much of subsequent AI research has focused on heuristics, which still play a major role in many practical AI applications, in the new millennium AI theory has finally become a full-fledged formal science, with important optimality results for embodied agents living in unknown environments, obtained through a combination of theory a la Goedel and probability theory. Here we look back at important milestones of AI history, mention essential recent results, and speculate about what we may expect from the next 25 years, emphasizing the significance of the ongoing dramatic hardware speedups, and discussing Goedel-inspired, self-referential, self-improving universal problem solvers.

This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62% as opposed to 58% obtained from a Bayesian formulated rough set model trained using Markov chain Monte Carlo method and 62% obtained from a Bayesian formulated multi-layered perceptron (MLP) model trained using hybrid Monte. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model. Keywords: Neuro-rough model, multi-layered perceptron, Bayesian, HIV modelling Introduction The role of machine learning is to be able to make predictions given a set of inputs.

Numerous formalisms and dedicated algorithms have been designed in the last decades to model and solve decision making problems. Some formalisms, such as constraint networks, can express "simple" decision problems, while others are designed to take into account uncertainties, unfeasible decisions, and utilities. Even in a single formalism, several variants are often proposed to model different types of uncertainty (probability, possibility...) or utility (additive or not). In this article, we introduce an algebraic graphical model that encompasses a large number of such formalisms: (1) we first adapt previous structures from Friedman, Chu and Halpern for representing uncertainty, utility, and expected utility in order to deal with generic forms of sequential decision making; (2) on these structures, we then introduce composite graphical models that express information via variables linked by "local" functions, thanks to conditional independence; (3) on these graphical models, we finally define a simple class of queries which can represent various scenarios in terms of observabilities and controllabilities. A natural decision-tree semantics for such queries is completed by an equivalent operational semantics, which induces generic algorithms. The proposed framework, called the Plausibility-Feasibility-Utility (PFU) framework, not only provides a better understanding of the links between existing formalisms, but it also covers yet unpublished frameworks (such as possibilistic influence diagrams) and unifies formalisms such as quantified boolean formulas and influence diagrams. Our backtrack and variable elimination generic algorithms are a first step towards unified algorithms.

We state an elementary inequality for the structure from motion problem for m cameras and n points. This structure from motion inequality relates space dimension, camera parameter dimension, the number of cameras and number points and global symmetry properties and provides a rigorous criterion for which reconstruction is not possible with probability 1. Mathematically the inequality is based on Frobenius theorem which is a geometric incarnation of the fundamental theorem of linear algebra. The paper also provides a general mathematical formalism for the structure from motion problem. It includes the situation the points can move while the camera takes the pictures.

Reputation mechanisms offer an effective alternative to verification authorities for building trust in electronic markets with moral hazard. Future clients guide their business decisions by considering the feedback from past transactions; if truthfully exposed, cheating behavior is sanctioned and thus becomes irrational. It therefore becomes important to ensure that rational clients have the right incentives to report honestly. As an alternative to side-payment schemes that explicitly reward truthful reports, we show that honesty can emerge as a rational behavior when clients have a repeated presence in the market. To this end we describe a mechanism that supports an equilibrium where truthful feedback is obtained. Then we characterize the set of pareto-optimal equilibria of the mechanism, and derive an upper bound on the percentage of false reports that can be recorded by the mechanism. An important role in the existence of this bound is played by the fact that rational clients can establish a reputation for reporting honestly.

We present and analyse three online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalisation error we draw learning curves in simplified situations. The performance for learning drifting concepts of one of the presented algorithms is analysed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented.

In this paper, we address the inverse problem of reconstructing a scene as well as the camera motion from the image sequence taken by an omni-directional camera. Our structure from motion results give sharp conditions under which the reconstruction is unique. For example, if there are three points in general position and three omni-directional cameras in general position, a unique reconstruction is possible up to a similarity. We then look at the reconstruction problem with m cameras and n points, where n and m can be large and the over-determined system is solved by least square methods. The reconstruction is robust and generalizes to the case of a dynamic environment where landmarks can move during the movie capture. Possible applications of the result are computer assisted scene reconstruction, 3D scanning, autonomous robot navigation, medical tomography and city reconstructions.

The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scholarly ontology that was engineered to represent those classes for which large-scale bibliographic and usage data exists, supports usage research, and whose instantiation is scalable to the order of 50 million articles along with their associated artifacts (e.g. authors and journals) and an accompanying 1 billion usage events. The real world instantiation of the presented abstract ontology is a semantic network model of the scholarly community which lends the scholarly process to statistical analysis and computational support. We present the ontology, discuss its instantiation, and provide some example inference rules for calculating various scholarly artifact metrics.

We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about "What is information?" In particular, what is "visual information" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of "information", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (Integer Linear Programming, Semidefinite Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search techniques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modeling and design of approximate solvers that combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype Inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modeling and hybrid metaheuristic solver design that can be generalized to other problems.