A Bayesian network for diagnosis of networked mobile robots

International audienceThe network of communicating mobile vehicles is a subclass of Wireless Networked Control Systems (WNCS) characterized by wireless communications and mobile nodes. The integration of the wireless network into control loop, given the stochastic aspects of wireless communication and mobility of its communicating entities, can lead to problems that affect system performances. In other words, the system quality of control QoC depends on the wireless network quality of service QoS state. A diagnosis method is essential to monitor, diagnose and maintain the system in an operational state. The present paper proposes a modular multi-layer Bayesian network model for diagnosis taking into account the network failures. Results regarding the system performance are presented to illustrate the relevance of the developed Bayesian Network BN to decisions making in order to lead the system to its final goal

Testing and improving local adaptive importance sampling in LJF local-JT in multiply sectioned Bayesian networks

Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems. The exact inference is costly and difficult to be applied in the context of MSBNs. So the approximate inference is used as an alternative. Recently, for reasoning in MSBNs, LJF-based Local Adaptive Importance Sampler (LLAIS) has been developed for approximate reasoning in MSBNs. However, the prototype of LLAIS is tested on Alarm Network (37 nodes). But further testing on larger networks has not been reported. In this thesis, LLAIS algorithm is tested on three large networks namely Hailfinder (56 nodes), Win95pts (76 nodes) and PathFinder (109 nodes), to measure for its reliability and scalability. The experiments done show that LLAIS without parameters tuned shows good convergence for Hailfinder and Win95pts but not for Pathfinder network. However, when the parameters are tuned the algorithm shows considerable improvement in its accuracy for all the three networks tested

Inference in distributed multiagent reasoning systems in cooperation with artificial neural networks

This research is motivated by the need to support inference in intelligent decision support systems offered by multi-agent, distributed intelligent systems involving uncertainty. Probabilistic reasoning with graphical models, known as Bayesian networks (BN) or belief networks, has become an active field of research and practice in artificial intelligence, operations research, and statistics in the last two decades. At present, a BN is used primarily as a stand-alone system. In case of a large problem scope, the large network slows down inference process and is difficult to review or revise. When the problem itself is distributed, domain knowledge and evidence has to be centralized and unified before a single BN can be created for the problem. Alternatively, separate BNs describing related subdomains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving, even if the interdependency relations are available. This issue has been investigated in several works, including most notably Multiply Sectioned BNs (MSBNs) by Xiang [Xiang93]. MSBNs provide a highly modular and efficient framework for uncertain reasoning in multi-agent distributed systems. Inspired by the success of BNs under the centralized and single-agent paradigm, a MSBN representation formalism under the distributed and multi-agent paradigm has been developed. This framework allows the distributed representation of uncertain knowledge on a large and complex environment to be embedded in multiple cooperative agents and effective, exact, and distributed probabilistic inference. What a Bayesian network is, how inference can be done in a Bayesian network under the single-agent paradigm, how multiple agents’ diverse knowledge on a complex environment can be structured as a set of coherent probabilistic graphical models, how these models can be transformed into graphical structures that support message passing, and how message passing can be performed to accomplish tasks in model compilation and distributed inference are covered in details in this thesis

Efficient Probabilistic Inference Algorithms for Cooperative Multiagent Systems

Probabilistic reasoning methods, Bayesian networks (BNs) in particular, have emerged as an effective and central tool for reasoning under uncertainty. In a multi-agent environment, agents equipped with local knowledge often need to collaborate and reason about a larger uncertainty domain. Multiply sectioned Bayesian networks (MSBNs) provide a solution for the probabilistic reasoning of cooperative agents in such a setting. In this thesis, we first aim to improve the efficiency of current MSBN exact inference algorithms. We show that by exploiting the calculation schema and the semantic meaning of inter-agent messages, we can significantly reduce an agent\u27s local computational cost as well as the inter-agent communication overhead. Our novel technical contributions include 1) a new message passing architecture based on an MSBN linked junction tree forest (LJF); 2) a suite of algorithms extended from our work in BNs to provide the semantic analysis of inter-agent messages; 3) a fast marginal calibration algorithm, designed for an LJF that guarantees exact results with a minimum local and global cost. We then investigate how to incorporate approximation techniques in the MSBN framework. We present a novel local adaptive importance sampler (LLAIS) designed to apply localized stochastic sampling while maintaining the LJF structure. The LLAIS sampler provides accurate estimations for local posterior beliefs and promotes efficient calculation of inter-agent messages. We also address the problem of online monitoring for cooperative agents. As the MSBN model is restricted to static domains, we introduce an MA-DBN model based on a combination of the MSBN and dynamic Bayesian network (DBN) models. We show that effective multi-agent online monitoring with bounded error is possible in an MA-DBN through a new secondary inference structure and a factorized representation of forward messages

Observations probabilistes dans les réseaux bayésiens

In a Bayesian network, evidence on a variable usually signifies that this variable is instantiated, meaning that the observer can affirm with certainty that the variable is in the signaled state. This thesis focuses on other types of evidence, often called uncertain evidence, which cannot be represented by the simple assignment of the variables. This thesis clarifies and studies different concepts of uncertain evidence in a Bayesian network and offers various applications of uncertain evidence in Bayesian networks.Firstly, we present a review of uncertain evidence in Bayesian networks in terms of terminology, definition, specification and propagation. It shows that the vocabulary is not clear and that some terms are used to represent different concepts.We identify three types of uncertain evidence in Bayesian networks and we propose the followingterminology: likelihood evidence, fixed probabilistic evidence and not-fixed probabilistic evidence. We define them and describe updating algorithms for the propagation of uncertain evidence. Finally, we propose several examples of the use of fixed probabilistic evidence in Bayesian networks. The first example concerns evidence on a subpopulation applied in the context of a geographical information system. The second example is an organization of agent encapsulated Bayesian networks that have to collaborate together to solve a problem. The third example concerns the transformation of evidence on continuous variables into fixed probabilistic evidence. The algorithm BN-IPFP-1 has been implemented and used on medical data from CHU Habib Bourguiba in Sfax.Dans un réseau bayésien, une observation sur une variable signifie en général que cette variable est instanciée. Ceci signifie que l’observateur peut affirmer avec certitude que la variable est dans l’état signalé. Cette thèse porte sur d’autres types d’observations, souvent appelées observations incertaines, qui ne peuvent pas être représentées par la simple affectation de la variable. Cette thèse clarifie et étudie les différents concepts d’observations incertaines et propose différentes applications des observations incertaines dans les réseaux bayésiens.Nous commençons par dresser un état des lieux sur les observations incertaines dans les réseaux bayésiens dans la littérature et dans les logiciels, en termes de terminologie, de définition, de spécification et de propagation. Il en ressort que le vocabulaire n'est pas clairement établi et que les définitions proposées couvrent parfois des notions différentes.Nous identifions trois types d’observations incertaines dans les réseaux bayésiens et nous proposons la terminologie suivante : observation de vraisemblance, observation probabiliste fixe et observation probabiliste non-fixe. Nous exposons ensuite la façon dont ces observations peuvent être traitées et propagées.Enfin, nous donnons plusieurs exemples d’utilisation des observations probabilistes fixes dans les réseaux bayésiens. Le premier exemple concerne la propagation d'observations sur une sous-population, appliquée aux systèmes d'information géographique. Le second exemple concerne une organisation de plusieurs agents équipés d'un réseau bayésien local et qui doivent collaborer pour résoudre un problème. Le troisième exemple concerne la prise en compte d'observations sur des variables continues dans un RB discret. Pour cela, l'algorithme BN-IPFP-1 a été implémenté et utilisé sur des données médicales de l'hôpital Bourguiba de Sfax

Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues

Le travail aborde la modélisation et l’analyse de systèmes complexes, caractérisés, dans un cadre incertain et évolutif, par de nombreuses interactions entre composants. Dans un cadre de gestion des risques, le travail vise à créer un modèle causal spatio-temporel pour l’explication et la probabilisation de certains événements redoutés à des fins de diagnostic et de pronostic. Le travail de modélisation est basé sur l’approche bayésienne et, plus particulièrement, sur les Réseaux Bayésiens (RB). Dans l’optique de considérer des systèmes de grande taille et de représenter leur complexité spatio-temporelle, leur caractère multiéchelles et incertain dans un cadre dynamique, l’idée de ce travail est alors d’étendre le concept des Réseaux Bayesiens Dynamiques (RBD) et d’utiliser le paradigme Orienté Objet pour l’appliquer aux réseaux bayésiens. Dans leur forme actuelle, les réseaux bayésiens orientés objet permettent de créer des instances réutilisables mais non instanciables. En d’autres termes, cela implique que l’objet généré est susceptible d’être appelé plusieurs fois dans la construction d’un modèle mais sans pour autant que ces paramètres (tables de probabilités marginales ou conditionnelles) ne soient modifiés. Ce travail de thèse vise à combler cette lacune en proposant une approche structurée permettant de construire des grands systèmes à base d’objets (caractérisés par des réseaux bayésiens simples) dont les paramètres peuvent être actualisés en fonction de l’instant ou du contexte de leur appel et de leur utilisation. Ce concept est concrétisé sous la forme de Réseaux Bayésiens Multidimensionnels par leur propension à combiner plusieurs dimensions qu’elles soient spatiales ou temporelles

A scalable, portable, FPGA-based implementation of the Unscented Kalman Filter

Sustained technological progress has come to a point where robotic/autonomous systems may well soon become ubiquitous. In order for these systems to actually be useful, an increase in autonomous capability is necessary for aerospace, as well as other, applications. Greater aerospace autonomous capability means there is a need for high performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a Field Programmable Gate Array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the Unscented Kalman Filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to retain portability, and a non-application-specific part, implemented in hardware as a parameterisable IP core to increase performance. The codesign is split into three versions (Serial, Parallel and Pipeline) to provide flexibility when choosing the balance between resources and performance, allowing system designers to simplify the development process. Simulation results demonstrating two possible implementations of the design, a nanosatellite application and a Simultaneous Localisation and Mapping (SLAM) application, are presented. These results validate the performance of the HW/SW UKF and demonstrate its portability, particularly in small aerospace systems. Implementation (synthesis, timing, power) details for a variety of situations are presented and analysed to demonstrate how the HW/SW codesign can be scaled for any application

Bayesian decision support in complex modular systems : an algebraic and graphical approach

Nowadays decision centres are required to make choices in complex and evolving environments, described through multiple and interdependent processes with many associated measurements. The objective of a real time decision making centre is to agree to a sequence of efficacious countermeasures. To achieve this it is usually necessary to integrate opinions and information from an often diverse set of stakeholders, articulating several competing objectives and knowledge over different domains of expertise. A collection of decision support systems can enhance such an integration, not only ensuring that all relevant evidence systematically informs policy making, but also encouraging the decision centre to exhibit an underlying consistency across all its components and to address the problem as a whole. In this thesis we develop a formal framework, extending standard Bayesian methodology, enabling the judgements and the models of groups of experts to be coherently aggregated in a unique entity. We discuss when and how it is possible to do so and the conditions the group needs to agree upon. We call this framework integrating decision support system. We then develop a variety of methodologies to enhance such an integration, enabling integrating decision support systems to be feasibly used in practice

Evaluation of Generative Models for Predicting Microstructure Geometries in Laser Powder Bed Fusion Additive Manufacturing

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex and cost-prohibitive. However, generative modes can provide new artificially generated data. Generative adversarial networks synthesize new computationally derived data through a process that learns the underlying features corresponding to the different laser process parameters in a generator network, then improves upon those artificial renderings by evaluating through the discriminator network. While this technique was effective at delivering high-quality images, modifications to the network through conditions showed improved capabilities at creating these new images. Using multiple evaluation metrics, it has been shown that generative models can be used to create new data for various laser process parameter combinations, thereby allowing a more comprehensive evaluation of ideal laser conditions for any particular build