Books by Pierre Bessiere
This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomou... more This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and dis- cussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which per- forms inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.
"CRC Press: http://www.crcpress.com/product/isbn/9781439880326
Features
• Presents a new mod... more "CRC Press: http://www.crcpress.com/product/isbn/9781439880326
Features
• Presents a new modeling methodology and inference algorithms for Bayesian programming
• Explains how to build efficient Bayesian models
• Addresses controversies, historical notes, epistemological debates, and tricky technical questions in a dedicated chapter separate from the main text
• Encourages further research on new programming languages and specialized hardware for computing large-scale Bayesian inference problems
• Offers an online Python package for running and modifying the Python program examples in the book
Summary
Probability as an Alternative to Boolean Logic
While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data.
Decision-Making Tools and Methods for Incomplete and Uncertain Data
Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.
Principles and Modeling
Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields.
Formalism and Algorithms
The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems.
FAQ / FAM
Along with a glossary, the fourth part contains answers to frequently asked questions and frequently argues matters. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability.
The First Steps toward a Bayesian Computer
A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.
... com Leitner Francois Aesculap SAS 24, rue Lamartine 38320 Eybens, FRANCE francois. leitner@ b... more ... com Leitner Francois Aesculap SAS 24, rue Lamartine 38320 Eybens, FRANCE francois. leitner@ bbraun. com Mazer Emmanuel Probayes Innovalle 345 rue Lavoisier 38330 St Ismier, FRANCE emazer@ probayes. ... pradalier@ ieee. org Guy Ramel SYNOVA SA Ch. ...
Papers by Pierre Bessiere
In this paper we describe a possible application of computing techniques inspired by natural life... more In this paper we describe a possible application of computing techniques inspired by natural life mechanisms (genetic algorithms and artificial neural networks) to an artificial life creature, namely a small mobile robot, called KitBorg. We proposed in a previous work (Bessiere 1990) Probabilistic Inference as a possible underlying theory or mathematical metaphor for numerous works in the field of formal neural networks. Probabilistic Inference suggests that any cognitive problem may be split in two optimization problems. The first one called "dynamic inference problem" is an abstraction of "learning", the second one, namely, the "static inference problem", being a mathematical metaphor of "pattern association". In this previous paper, for instance, Boltzmann machines have been shown to be a special case of probabilistic inference, where the two optimization problems are dealt with using simulated annealing (Kirckpatrick 1983) for the pattern association part and using simple gradient descent for the learning one. It was, then, suggested that other optimization technics should be considered in that context and especially genetic algorithms. The purpose of this paper is to describe the state of the art of the investigations we are making about that question using a parallel genetic algorithm. We will first recall the principles of probabilistic inference, then , we will present briefly the parallel genetic algorithm and the ways it is used to deal with both optimization problems, to finally conclude about ongoing robotic experimentations and future planned extensions.
International Conference on Robotics and Automation, 1995
A computational approach to direct and generalizedinverse model acquisition is presented. The app... more A computational approach to direct and generalizedinverse model acquisition is presented. The approach isbased on a proposed method to direct model acquisitionfrom partial information. The method decomposes anhyper-space function in one variable functions, simplifyingthe learning problem. The acquired direct model is thenimplemented in a tree-like structure that can be used in theinverse sense without additional learning effort.Our approach is able
Autonomous navigation of a mobile robot along a predefined tr ajectory is a widely studied proble... more Autonomous navigation of a mobile robot along a predefined tr ajectory is a widely studied problem in the robotics community. As an a lternative to Cartesian trajectories, we propose to define trajectories as sequence s of sensory-motor information. In this paper we show that such a representation is suitable f or navigating on the trajectory. To this end, we describe an architecture of Bayesian inferen c to localize and control the robot on its trajectory. In order to increase robustness, we also use this Bayesian framework to estimate system self-confidence while the robot is moving . This work has been validated both on a simulated robot and on a real car-like robot: the CyC ab.
Second International Conference on Simulation and Adaptative Behavior (SAB92), Honolulu (USA), 1992
HAL - hal.archives-ouvertes.fr, CCSd - Centre pour la Communication Scientifique Direct. Accueil;... more HAL - hal.archives-ouvertes.fr, CCSd - Centre pour la Communication Scientifique Direct. Accueil; Dépôt: S'authentifier; S'inscrire. Consultation: Par domaine; Les 30 derniers dépôts; Par année de publication, rédaction, dépôt; Par type de publication; Par collection; Les portails de l'archive ouverte HAL; Par établissement (extraction automatique); ArXiv; Les Thèses (TEL). Recherche: Recherche simple; Recherche avancée; Accès par identifiant; Les Thèses ...
Among the various possible criteria guiding eye movement selection, we investigate the role of po... more Among the various possible criteria guiding eye movement selection, we investigate the role of position uncer-tainty in the peripheral visual field. In particular, we suggest that, in everyday life situations of object tracking, eye move-ment selection probably includes a principle of reduction of uncertainty. To evaluate this hypothesis, we confront the movement predictions of computational models with human results from a psychophysical task. This task is a freely mov-ing eye version of the multiple object tracking task, where the eye movements may be used to compensate for low periph-eral resolution. We design several Bayesian models of eye movement selection with increasing complexity, whose lay-ered structures are inspired by the neurobiology of the brain areas implied in this process. Finally, we compare the relative performances of these models with regard to the prediction of the recorded human movements, and show the advantage of
The goal of the work described in this paper is to build a path planner able to drive a robot in ... more The goal of the work described in this paper is to build a path planner able to drive a robot in a dynamic environment where the obstacles are moving. In order to do so, we propose a method, called "ARIADNE'S CLEW algorithm", to build a global path planner based on the combination of two local planning algorithms : an EXPLORE algorithm and a SEARCH algorithm. The purpose of the EXPLORE algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the SEARCH algorithm is to opportunistically check if the target can be easily reached from any given placed landmark. The ARIADNE'S CLEW algorithm is shown to be very fast in most cases allowing planning in dynamic environments. Hence, it is shown to be complete, which means that it is sure to find a path when one exists. Finally, we describe a massively parallel implementation of this algorithm.
Uploads
Books by Pierre Bessiere
Features
• Presents a new modeling methodology and inference algorithms for Bayesian programming
• Explains how to build efficient Bayesian models
• Addresses controversies, historical notes, epistemological debates, and tricky technical questions in a dedicated chapter separate from the main text
• Encourages further research on new programming languages and specialized hardware for computing large-scale Bayesian inference problems
• Offers an online Python package for running and modifying the Python program examples in the book
Summary
Probability as an Alternative to Boolean Logic
While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data.
Decision-Making Tools and Methods for Incomplete and Uncertain Data
Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.
Principles and Modeling
Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields.
Formalism and Algorithms
The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems.
FAQ / FAM
Along with a glossary, the fourth part contains answers to frequently asked questions and frequently argues matters. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability.
The First Steps toward a Bayesian Computer
A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.
Papers by Pierre Bessiere
Features
• Presents a new modeling methodology and inference algorithms for Bayesian programming
• Explains how to build efficient Bayesian models
• Addresses controversies, historical notes, epistemological debates, and tricky technical questions in a dedicated chapter separate from the main text
• Encourages further research on new programming languages and specialized hardware for computing large-scale Bayesian inference problems
• Offers an online Python package for running and modifying the Python program examples in the book
Summary
Probability as an Alternative to Boolean Logic
While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data.
Decision-Making Tools and Methods for Incomplete and Uncertain Data
Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.
Principles and Modeling
Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields.
Formalism and Algorithms
The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems.
FAQ / FAM
Along with a glossary, the fourth part contains answers to frequently asked questions and frequently argues matters. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability.
The First Steps toward a Bayesian Computer
A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.