Special Issue "Bayesian Networks: Inference Algorithms, Applications, and Software Tools"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 30 July 2020.

Special Issue Editor

Dr. Daniele Codetta Raiteri
Website
Guest Editor
Computer Science Institute, DiSIT, University of Piemonte Orientale, Alessandria, Italy
Interests: probabilistic graphical models; reliability; risk analysis; security
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of Artificial Intelligence, Bayesian Networks (BN) are a well-known framework for reasoning under uncertain knowledge. BN have been applied in a wide range of real-world domains, such as medical diagnosis, forensic analysis, dependability assessment, risk management, etc. With respect to other types of models, BN provide relevant advantages: at the modelling level, the compact representation of the joint distribution of the system variables leads to the factorization of the set of possible states, avoiding the generation of the complete state space of the system; at the analysis level, inference algorithms can compute the probability distribution of any variable, possibly conditioned on the observation of the value (state) of other variables, so that predictive and diagnostic measures can be easily evaluated. During the years, BN have been extended in order to increase their modelling and analysis power; for instance, Dynamic Bayesian Networks and Continuous-Time Bayesian Networks take time into account, Hybrid Bayesian Networks deal with both discrete and continuous variables, Decision Networks contain decision nodes and value nodes.

The aim of this Special Issue is to collect recent developments about inference algorithms, their applications to real-case studies, and their implementation in software tools. The topics include, but are not limited to, the following:

  • BN extensions
  • Inference algorithms for BN extensions
  • Automatic generation of BN from higher-level models
  • Software tools or libraries
  • Applications to real-case studies

Dr. Daniele Codetta Raiteri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • BN extensions
  • Inference algorithms for BN extensions
  • Automatic generation of BN from higher-level models
  • Software tools or libraries
  • Applications to real-case studies.

Published Papers (2 papers)

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Research

Open AccessArticle
Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
Algorithms 2020, 13(7), 155; https://doi.org/10.3390/a13070155 - 28 Jun 2020
Abstract
Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We [...] Read more.
Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits. Full article
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Open AccessArticle
A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems
Algorithms 2020, 13(3), 64; https://doi.org/10.3390/a13030064 - 12 Mar 2020
Abstract
Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling [...] Read more.
Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: Embedded Bayesian Network Contribution for a Safe Missing Planning of Autonomous Vehicle
Authors:  Catherine Dezan, Sara Zermani and Chabha Hireche
Abstract: Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in  numerous domains (medicine, finance, transport, robotics, ...). In a case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the  elaboration of  FDIR (Fault Detection Isolation and Recovery) modules. One of the main difficulty  with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some  automatic generation techniques from FMEA-like (Failure Mode and Effects Analysis) tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. So in a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of  unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and  the BFM model that is an extension of the MDP (Markov Decision Process) decision-making model incorporating BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits.

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