Multiply Sectioned Bayesian Networks and Junction Forests for Large Knowledge‐based Systems

Xiang, Yang; Poole, David; Beddoes, Michael P.

doi:10.1111/j.1467-8640.1993.tb00306.x

Cited by 80 publications

(76 citation statements)

References 11 publications

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“…As the agent shifts attention to each subnet and enters evidence, ShiftAttention (Section 2) maintains (local) consistency along the hyperpath in the hypertree structured forest. It is proven [12] that such local consistency is actually at the global level, i.e., answers to queries at the current JT is consistent with the evidence acquired in the entire LJF.…”

Section: Consistency Issues In Multi-agent Msbnsmentioning

confidence: 97%

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Distributed multi-agent probabilistic reasoning with Bayesian networks

Xiang

1994

Lecture Notes in Computer Science

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Abstract. Main stream approaches in distributed artificial intelligence (DAI) are essentially logic-based. Little has been reported to explore probabilistic approach in DAI. On the other hand, Bayesian networks have been applied to many AI tasks that require reasoning under uncertainty. However, as commonly applied, a single-agent paradigm is assumed in Bayesian networks. This paper extends multiply sectioned Bayesian networks (MSBNs) for single-agent systems into a framework for multi-agent distributed interpretation systems. Each cooperative agent is represented as a Bayesian subnet that consumes its own computational resource, gathers its own evidence, and can answer queries. Unlike in single-agent systems where evidence is entered one subnet at a time, multiple agents may acquire evidence in parallel. We add to the set of single-agent MSBN operations new belief propagation operations which, when performed, regain global consistency. Due to the inter-agent 'distance' and the associated communication cost, global consistency can not be maintained constantly. We show that, when the proposed operations are followed, between two successive communications, the answers to queries from an agent are consistent with all local evidence gathered so far, and are consistent with all global evidence gathered up to the last communication.

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Section: Consistency Issues In Multi-agent Msbnsmentioning

confidence: 97%

“…In the following, we briefly introduce single-agent oriented MSBNs. For a formal presentation of MSBNs, see [12]. A MSBN consists of a set of interrelated Bayesian subnets.…”

Section: Single Agent Oriented Msbnsmentioning

confidence: 99%

Distributed multi-agent probabilistic reasoning with Bayesian networks

Xiang

1994

Lecture Notes in Computer Science

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“…As the complexity of the diagnosis scenarios grows, scalability may become a problem. To support this, the system is able to distribute the inference process in several smaller Bayesian networks [9] to delegate parts of diagnosis in agents specialized in different problems, regions, services, etc. As mentioned above, the inference process combines two inference strategies.…”

Section: Distributed Bayesian Reasoningmentioning

confidence: 99%

A Multi-Agent System with Distributed Bayesian Reasoning for Network Fault Diagnosis

Carrera

Gonzalez-Ordás

García-Algarra

et al. 2011

Advances in Intelligent and Soft Computing

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In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.

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“…OOBNs [16] and Multiply Sectioned Bayesian Networks (MSBNs) [17], [18] are different paradigms that add the ability to perform such grouping within a BN model. In MSBNs the entire model is divided into sections, which each have their own computational unit that acts autonomously.…”

Section: B Nested Structures In Bayesian Networkmentioning

confidence: 99%

A methodology for developing local smart diagnostic models using expert knowledge

Madsen

Søndberg-Jeppesen

Lohse

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Abstract-This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the componentbased modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular componentbased modelling in manufacturing and other domains.

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Multiply Sectioned Bayesian Networks and Junction Forests for Large Knowledge‐based Systems

Cited by 80 publications

References 11 publications

Distributed multi-agent probabilistic reasoning with Bayesian networks

Distributed multi-agent probabilistic reasoning with Bayesian networks

A Multi-Agent System with Distributed Bayesian Reasoning for Network Fault Diagnosis

A methodology for developing local smart diagnostic models using expert knowledge

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