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Title: On combinations of local theory extensions |
Abstract: In this paper we study possibilities of efficient reasoning in combinations of theories over possibly non-disjoint signatures. We first present a class of theory extensions (called local extensions) in which hierarchical reasoning is possible, and give several examples from computer science and mathematics in... |
Title: Path Planner for Objects, Robots and Mannequins by Multi-Agents Systems or Motion Captures |
Abstract: In order to optimise the costs and time of design of the new products while improving their quality, concurrent engineering is based on the digital model of these products. However, in order to be able to avoid definitively physical model without loss of information, new tools must be available. Especially, a... |
Title: A Vision-based Computed Torque Control for Parallel Kinematic Machines |
Abstract: In this paper, a novel approach for parallel kinematic machine control relying on a fast exteroceptive measure is implemented and validated on the Orthoglide robot. This approach begins with rewriting the robot models as a function of the only end-effector pose. It is shown that such an operation reduces the ... |
Title: A Simple Linear Ranking Algorithm Using Query Dependent Intercept Variables |
Abstract: The LETOR website contains three information retrieval datasets used as a benchmark for testing machine learning ideas for ranking. Algorithms participating in the challenge are required to assign score values to search results for a collection of queries, and are measured using standard IR ranking measures (... |
Title: A comparison of the notions of optimality in soft constraints and graphical games |
Abstract: The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of two formalisms used for different purposes and in different research areas: graphical games and soft constraints. We relate the not... |
Title: Combining Semantic Wikis and Controlled Natural Language |
Abstract: We demonstrate AceWiki that is a semantic wiki using the controlled natural language Attempto Controlled English (ACE). The goal is to enable easy creation and modification of ontologies through the web. Texts in ACE can automatically be translated into first-order logic and other languages, for example OWL. ... |
Title: On the Vocabulary of Grammar-Based Codes and the Logical Consistency of Texts |
Abstract: The article presents a new interpretation for Zipf-Mandelbrot's law in natural language which rests on two areas of information theory. Firstly, we construct a new class of grammar-based codes and, secondly, we investigate properties of strongly nonergodic stationary processes. The motivation for the joint di... |
Title: On the Complexity of Core, Kernel, and Bargaining Set |
Abstract: Coalitional games are mathematical models suited to analyze scenarios where players can collaborate by forming coalitions in order to obtain higher worths than by acting in isolation. A fundamental problem for coalitional games is to single out the most desirable outcomes in terms of appropriate notions of wo... |
Title: Inferring sparse Gaussian graphical models with latent structure |
Abstract: Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables. We describe a novel framework taking into account a latent structure on the ... |
Title: Quantum robot: structure, algorithms and applications |
Abstract: This paper has been withdrawn. |
Title: Text as Statistical Mechanics Object |
Abstract: In this article we present a model of human written text based on statistical mechanics approach by deriving the potential energy for different parts of the text using large text corpus. We have checked the results numerically and found that the specific heat parameter effectively separates the closed class w... |
Title: Detecting the Most Unusual Part of a Digital Image |
Abstract: The purpose of this paper is to introduce an algorithm that can detect the most unusual part of a digital image. The most unusual part of a given shape is defined as a part of the image that has the maximal distance to all non intersecting shapes with the same form. The method can be used to scan image databa... |
Title: Language structure in the n-object naming game |
Abstract: We examine a naming game with two agents trying to establish a common vocabulary for n objects. Such efforts lead to the emergence of language that allows for an efficient communication and exhibits some degree of homonymy and synonymy. Although homonymy reduces the communication efficiency, it seems to be a ... |
Title: The many faces of optimism - Extended version |
Abstract: The exploration-exploitation dilemma has been an intriguing and unsolved problem within the framework of reinforcement learning. "Optimism in the face of uncertainty" and model building play central roles in advanced exploration methods. Here, we integrate several concepts and obtain a fast and simple algorit... |
Title: Social Learning Methods in Board Games |
Abstract: This paper discusses the effects of social learning in training of game playing agents. The training of agents in a social context instead of a self-play environment is investigated. Agents that use the reinforcement learning algorithms are trained in social settings. This mimics the way in which players of b... |
Title: The use of entropy to measure structural diversity |
Abstract: In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity ind... |
Title: Hierarchical Bag of Paths for Kernel Based Shape Classification |
Abstract: Graph kernels methods are based on an implicit embedding of graphs within a vector space of large dimension. This implicit embedding allows to apply to graphs methods which where until recently solely reserved to numerical data. Within the shape classification framework, graphs are often produced by a skeleto... |
Title: A Minimum Relative Entropy Principle for Learning and Acting |
Abstract: This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is an agent that has been designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy ... |
Title: Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling |
Abstract: The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Different strategies have been proposed to solve this problem, however, rigorous analysis justifying their performance is missing. This paper suggests the Theoretical Spectral Curvature Clustering (TS... |
Title: Quantum reinforcement learning |
Abstract: The key approaches for machine learning, especially learning in unknown probabilistic environments are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the sta... |
Title: Astronomical imaging: The theory of everything |
Abstract: We are developing automated systems to provide homogeneous calibration meta-data for heterogeneous imaging data, using the pixel content of the image alone where necessary. Standardized and complete calibration meta-data permit generative modeling: A good model of the sky through wavelength and time--that is,... |
Title: Relationship between Diversity and Perfomance of Multiple Classifiers for Decision Support |
Abstract: The paper presents the investigation and implementation of the relationship between diversity and the performance of multiple classifiers on classification accuracy. The study is critical as to build classifiers that are strong and can generalize better. The parameters of the neural network within the committ... |
Title: Multistage Hypothesis Tests for the Mean of a Normal Distribution |
Abstract: In this paper, we have developed new multistage tests which guarantee prescribed level of power and are more efficient than previous tests in terms of average sampling number and the number of sampling operations. Without truncation, the maximum sampling numbers of our testing plans are absolutely bounded. Ba... |
Title: Estimating high-dimensional intervention effects from observational data |
Abstract: We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimat... |
Title: Efficient Exact Inference in Planar Ising Models |
Abstract: We give polynomial-time algorithms for the exact computation of lowest-energy (ground) states, worst margin violators, log partition functions, and marginal edge probabilities in certain binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm i... |
Title: Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy |
Abstract: In this paper we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is a... |
Title: Online Coordinate Boosting |
Abstract: We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than previous online boosting algorithms. We also contribute a new way of deriving the online algorithm that ties together previous onlin... |
Title: Learning Isometric Separation Maps |
Abstract: Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensional spaces, often revealing the true intrinsic dimension. In this paper we show how to also incorporate supervised class information into an MVU-like method without breaking its convexity. ... |
Title: Assembling Actor-based Mind-Maps from Text Stream |
Abstract: For human beings, the processing of text streams of unknown size leads generally to problems because e.g. noise must be selected out, information be tested for its relevance or redundancy, and linguistic phenomenon like ambiguity or the resolution of pronouns be advanced. Putting this into simulation by using... |
Title: Graph-based classification of multiple observation sets |
Abstract: We consider the problem of classification of an object given multiple observations that possibly include different transformations. The possible transformations of the object generally span a low-dimensional manifold in the original signal space. We propose to take advantage of this manifold structure for the... |
Title: On Granular Knowledge Structures |
Abstract: Knowledge plays a central role in human and artificial intelligence. One of the key characteristics of knowledge is its structured organization. Knowledge can be and should be presented in multiple levels and multiple views to meet people's needs in different levels of granularities and from different perspec... |
Title: Robust Estimation of Mean Values |
Abstract: In this paper, we develop a computational approach for estimating the mean value of a quantity in the presence of uncertainty. We demonstrate that, under some mild assumptions, the upper and lower bounds of the mean value are efficiently computable via a sample reuse technique, of which the computational comp... |
Title: Statistical Learning Theory: Models, Concepts, and Results |
Abstract: Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning resear... |
Title: A SURE Approach for Digital Signal/Image Deconvolution Problems |
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