117 research outputs found
Interface tactile pour la saisie guidée de connaissances
International audienceIn recent years, artificial intelligence tools have democratized and are increasingly used by people who are not experts in the field. These artificial intelligence tools, like rule-based or constraint-based systems require the input of human expertise to replicate the desired reasoning. Despite the explosion of new devices and new input paradigms, such as tablets and other touch interfaces, it seems that the usability of these tools have not taken advantage of these recent advances. In this article, we illustrate our concept with the rule edition in a fuzzy expert system. The special feature of fuzzy logic is that these rules look closer to natural language than classical logic. We present our work that involves the use of new touch interfaces to edit a fuzzy rule base with one finger. We end this section by the evaluation of the interface with a user panel.Au cours de ces dernières années, les outils d'intelligence artificielle se sont démocratisés et sont de plus en plus sou-vent utilisés par des personnes qui ne sont pas expertes du domaine. Parmi ces outils d'intelligence artificielle, les systèmes à base de règles ou de contraintes nécessitent la saisie de l'expertise humaine afin de reproduire le comporte-ment souhaité. Malgré l'explosion des nouveaux périphé-riques et de nouveaux paradigmes de saisie, comme les tablettes et autres interfaces tactiles, l'ergonomie de ces outils semble ne pas avoir profité de toutes ces avancées récentes. Dans cet article, nous prenons l'exemple d'un système expert flou pour lequel il faut rédiger des règles. La particu-larité de la logique floue est que ces règles sont construites d'une manière plus proche du langage naturel qu'en lo-gique classique. Nous présentons notre travail qui consiste en l'exploitation des nouvelles interfaces tactiles afin de rédiger une base de règles floues avec un seul doigt. Nous terminons cet article par l'évaluation de l'interface auprès d'un panel d'utilisateurs
Measuring Relations Between Concepts In Conceptual Spaces
The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
Our recent mathematical formalization of this framework is capable of
representing correlations between different domains in a geometric way. In this
paper, we extend our formalization by providing quantitative mathematical
definitions for the notions of concept size, subsethood, implication,
similarity, and betweenness. This considerably increases the representational
power of our formalization by introducing measurable ways of describing
relations between concepts.Comment: Accepted at SGAI 2017 (http://www.bcs-sgai.org/ai2017/). The final
publication is available at Springer via
https://doi.org/10.1007/978-3-319-71078-5_7. arXiv admin note: substantial
text overlap with arXiv:1707.05165, arXiv:1706.0636
Learning Tversky Similarity
In this paper, we advocate Tversky's ratio model as an appropriate basis for
computational approaches to semantic similarity, that is, the comparison of
objects such as images in a semantically meaningful way. We consider the
problem of learning Tversky similarity measures from suitable training data
indicating whether two objects tend to be similar or dissimilar.
Experimentally, we evaluate our approach to similarity learning on two image
datasets, showing that is performs very well compared to existing methods
Novel hybrid adaptive controller for manipulation in complex perturbation environments
© 2015 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing
Formalized Conceptual Spaces with a Geometric Representation of Correlations
The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
similarity space and concepts are represented by convex regions in this space.
After pointing out a problem with the convexity requirement, we propose a
formalization of conceptual spaces based on fuzzy star-shaped sets. Our
formalization uses a parametric definition of concepts and extends the original
framework by adding means to represent correlations between different domains
in a geometric way. Moreover, we define various operations for our
formalization, both for creating new concepts from old ones and for measuring
relations between concepts. We present an illustrative toy-example and sketch a
research project on concept formation that is based on both our formalization
and its implementation.Comment: Published in the edited volume "Conceptual Spaces: Elaborations and
Applications". arXiv admin note: text overlap with arXiv:1706.06366,
arXiv:1707.02292, arXiv:1707.0516
Operations with Fuzzy Numbers Explain Heuristic Methods in Image Processing
Maximum entropy method and its heuristic generalizations are very useful in image processing. In this paper, we show that the use of fuzzy numbers enables us to naturally explain these heuristic methods. 1 Introduction to the problem 1.1 The main objective of image processing In many problems of image processing, e.g., in optical and radio astronomy, we want to reconstruct the image. In precise terms, we want to know the function I(~oe) that describes how brightness depends on the coordinates ~oe = (x; y). Most of the time, we are only interested in the values of brightness over a sufficiently dense rectangular grid that is formed by the points ~oe ij = (x i ; y j ) with coordinates x i = x 0 + i \Delta h x and y j = y 0 + j \Delta h y . The corresponding values I(~oe ij ) of the brightness function form a matrix; the components of this matrix are, for simplicity, usually denoted as I ij . 1.2 The main problem of image processing: general formulation Real-life measurements are..
Approximate reasoning with linguistic modifiers
We analyze the influence of some usual linguistic modifiers, such as scalar product, normalization, Bouchon-Meunier modifiers, perturbation, and (weakening and reinforcement) power, in the process of approximate reasoning and clarify the difference between the conclusions of fuzzy modus ponens in which linguistic modifiers appear and do not appear in premises. © 1998 John Wiley & Sons, Inc
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