Bayesian model comparison and distinguishability

International audienceThis paper focuses on Bayesian modeling applied to the experimental methodology. More precisely, we consider Bayesian model comparison and selection, and the distinguishability of models, that is, the ability to discriminate between alternative theoretical explanations of experimental data. We argue that this last concept should be central, but is difficult to manipulate with existing model comparison approaches. Therefore, we propose a preliminary extension of the Bayesian model selection method that incorporates model distinguishability, and illustrate it on an example of modeling the planning of arm movements in humans

Bayesian robot Programming

We propose a new method to program robots based on Bayesian inference and learning. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of this approach are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics

BBPRM: a behavior-based probabilistic roadmap method

International audienceThis paper focuses on the path planning problem. We offer an alternative to the probabilistic roadmap methods, from the perspective of modeling human or animal planning. In this context, hierarchies of representations are used to break down high-dimensional configuration spaces. We propose an approach for roadmap generation where low-level behaviors are used as articulations between level of the hierarchy. We also show how the obtained roadmap better represents low-level sensorimotor capabilities of the robot

Bayesian Maps: probabilistic and hierarchical models for mobile robot navigation

What is a map? What is its utility? What is a location, a behaviour? What are navigation, localization and prediction for a mobile robot facing a given task ? These questions have neither unique nor straightforward answer to this day, and are still the core of numerous research domains. Robotics, for instance, aim at answering them for creating successful sensori-motor artefacts. Cognitive sciences use these questions as intermediate goals on the road to un- derstanding living beings, their skills, and furthermore, their intelligence. Our study lies between these two domains. We first study classical probabilistic ap- proaches (Markov localization, POMDPs, HMMs, etc.), then some biomimetic approaches (Berthoz, Franz, Kuipers). We analyze their respective advantages and drawbacks in light of a general formalism for robot programming based on bayesian inference (BRP). We propose a new probabilistic formalism for modelling the interaction between a robot and its environment : the Bayesian map. In this framework, defining a map is done by specifying a particular probability distri- bution. Some of the questions above then amount to solving inference problems. We define operators for putting maps together, so that " hierarchies of maps " and incremental development play a central role in our formalism, as in biomimetic approaches. By using the bayesian formalism, we also benefit both from a unified means of dealing with uncertainties, and from clear and rigorous mathematical foundations. Our formalism is illustrated by experiments that have been implemented on a Koala mobile robot

Multiple object manipulation: is structural modularity necessary? A study of the MOSAIC and CARMA models

International audienceA model that tackles the Multiple Object Manipulation task computationally solves a higly complex cognitive task. It needs to learn how to identify and predict the dynamics of various physical objects in different contexts in order to manipulate them. MOSAIC is a model based on the modularity hypothesis: it relies on multiple controllers, one for each object. In this paper we question this modularity characteristic. More precisely, we show that the MOSAIC convergence during learning is quite sensitive to parameter values. To solve this issue, we define another model (CARMA) which tackles the manipulation problem with a single controller. We provide experimental and theoretical evidence that tend to indicate that non-modularity is the most natural hypothesis

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

Bayesian modeling of human performance in a visual processing training software

International audienceDyslexia is a deficit of the identification of words, which is thought to be a consequence of different possible cognitive impairments. Recent data suggest that one of these might be a specific deficit of the visual attention span (VAS). We are developing a remediation software for dyslexic children that focuses on the VAS and its training. A central component of this software is the estimation of the performance of a given participant for all possible exercises. We describe a preliminary probabilistic model of participant performance, based on Bayesian modeling and inference. We mathematically define the model, making explicit underlying generalization hypotheses. The model yields a computation of the most probable predicted performance space, and, as a direct extension, an exercise selection strategy

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

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach

International audienceThe remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the Central Nervous System selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way

Bayesian Robot Programming

International audienceWe propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of BRP are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics