Modèles probabilistes formels pour problèmes cognitifs usuels

International audienceHow can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.Comment un modèle incomplet et incertain de l'environnement peut-il être utilisé pour décider, agir, apprendre, raisonner et percevoir efficacement ? Voici le défi central que les systèmes cognitifs tant naturels qu'artificiels doivent résoudre. La logique, de par sa nature même, faite de certitudes et ne laissant aucune place au doute, est incapable de répondre à cette question. L'approche subjectiviste des probabilités est une extension de la logique conçue pour pallier ce manque. Dans cet article, nous passons en revue un ensemble de problèmes cognitifs usuels et nous montrons comment les formuler et les résoudre avec un formalisme probabiliste unique. Les concepts abordés sont : l'ambigüité, la fusion, la multi-modalité, les conflits, la modularité, les hiérarchies et les boucles. Chacune de ces questions est tout d'abord brièvement présentée à partir d'exemples venant des neurosciences, de la psychophysique ou de la robotique. Ensuite, le concept est formalisé en utilisant un modèle générique bayésien. Enfin, les hypothèses, les points communs et les différences de chacun de ces modèles sont analysés et discutés

Common Bayesian Models for Common Cognitive Issues

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discusse

ACES: Generating Diverse Programming Puzzles with Autotelic Language Models and Semantic Descriptors

Finding and selecting new and interesting problems to solve is at the heart of curiosity, science and innovation. We here study automated problem generation in the context of the open-ended space of python programming puzzles. Existing generative models often aim at modeling a reference distribution without any explicit diversity optimization. Other methods explicitly optimizing for diversity do so either in limited hand-coded representation spaces or in uninterpretable learned embedding spaces that may not align with human perceptions of interesting variations. With ACES (Autotelic Code Exploration via Semantic descriptors), we introduce a new autotelic generation method that leverages semantic descriptors produced by a large language model (LLM) to directly optimize for interesting diversity, as well as few-shot-based generation. Each puzzle is labeled along 10 dimensions, each capturing a programming skill required to solve it. ACES generates and pursues novel and feasible goals to explore that abstract semantic space, slowly discovering a diversity of solvable programming puzzles in any given run. Across a set of experiments, we show that ACES discovers a richer diversity of puzzles than existing diversity-maximizing algorithms as measured across a range of diversity metrics. We further study whether and in which conditions this diversity can translate into the successful training of puzzle solving models

Common bayesian models for common cognitive issues

International audienceHow can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed

Characterization of the phonon sensor of the CRYOSEL detector with IR photons

The sensitivities of light Dark Matter (DM) particle searches with cryogenic detectors are mostly limited by large backgrounds of events that do not produce ionization signal. The CRYOSEL project develops a new technique where this background in a germanium cryogenic detector is rejected by using the signals from a Superconducting Single Electron Device (SSED) sensor designed to detect the phonons emitted through the Neganov-Trofimov-Luke effect by the e$^-$h$^+$ pairs as they drift in a close-by very high-field region. A tag on signals from this device should suppress the heat-only background. The measurement of the response to IR laser pulses of the first CRYOSEL prototype show the relevance of such sensor technology.Comment: 9 pages, 3 figures, LTD2

Sur la rupture expérimentale des verres monolithiques pincés

Ce travail propose une étude paramétrique sur le dimensionnement des verres monolithiques pincés. Dans le cadre de cette étude, un modèle numérique a été développé afin de déterminer les contraintes de rupture dans le vitrage au voisinage des pinces. Ce modèle repose sur une campagne d'essais effectués sur des plaques de verres pincés. Le modèle prend en compte les types de produit verrier, les caractéristiques des pinces, ainsi que le comportement des intercalaires en EPDM (supposé élastique linéaire) entre les pinces et la plaque de verre. Les assemblages sont testés en flexion trois points en prenant en compte deux types de verre (trempé et recuit). Les essais de flexion sont menés jusqu'à la rupture totale de la structure verrière. La force appliquée ainsi que la flèche à la rupture sont relevées et comparées au modèle théorique. Le mode de fissuration du verre recuit est également analysé. Cette analyse met en évidence des phénomènes de rupture du verre monolithique différents selon les paramètres, par exemple la dureté de l'intercalaire. Parallèlement, un phénomène de point dur est identifié et localisé en fonction des différents paramètres précédemment identifiés. Ce phénomène est comparé à la répartition des contraintes dans le produit verrier du modèle théorique. La corrélation entre le modèle numérique et les résultats expérimentaux montre des écarts variables en fonction du type de verre et de l'épaisseur expliqués en partie, par le comportement non-linéaire de l'intercalaire

SOL RF physics modelling in Europe, in support of ICRF experiments

A European project was undertaken to improve the available SOL ICRF physics simulation tools and confront them with measurements. This paper first reviews code upgrades within the project. Using the multi-physics finite element solver COMSOL, the SSWICH code couples RF full-wave propagation with DC plasma biasing over “antenna-scale” 2D (toroidal/radial) domains, via non-linear RF and DC sheath boundary conditions (SBCs) applied at shaped plasma-facing boundaries. For the different modules and associated SBCs, more elaborate basic research in RF-sheath physics, SOL turbulent transport and applied mathematics, generally over smaller spatial scales, guides code improvement. The available simulation tools were applied to interpret experimental observations on various tokamaks. We focus on robust qualitative results common to several devices: the spatial distribution of RF-induced DC bias; left-right asymmetries over strap power unbalance; parametric dependence and antenna electrical tuning; DC SOL biasing far from the antennas, and RF-induced density modifications. From these results we try to identify the relevant physical ingredients necessary to reproduce the measurements, e.g. accurate radiated field maps from 3D antenna codes, spatial proximity effects from wave evanescence in the near RF field, or DC current transport. Pending issues towards quantitative predictions are also outlined

Interaction Fields: Sketching Collective Behaviours

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