Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Subject: search.filters.subject.belief networks

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Now showing 1 - 5 of 5
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    Intelligent Distributed Fault Management for Communication Networks
    (2000) Li, Hongjun; Baras, John S.; ISR; CSHCN
    In this paper, we present an intelligent, distributed fault management system for communication networks using belief networks as fault model and inference engine. The managed network is divided into domains and for each domain, there is an intelligent agent called Domain Diagnostic Agent attached to it, which is responsible for this domain's fault management. Belief network models are embedded in such an agent and under symptoms observation, the posterior probabilities of each candidate fault node being faulty is computed. We define the notion of right diagnosis, describe the diagnosis process based on this concept, and present a strategy for generation of test sequence.
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    Statistical Parameter Learning for Belief Networks with Fixed Structure
    (1999) Li, Hongjun; Baras, John S.; ISR; CSHCN
    In this report, we address the problem of parameter learning for belief networks with fixed structure based on empirical observations. Both complete and incomplete (data) observations are included. Given complete data, we describe the simple problem of single parameter learning for intuition and then expand to belief networks under appropriate system decomposition. If the observations are incomplete, we first estimate the "missing" observations and treat them as though they are "real" observations, based on which the parameter learning can be executed as in complete data case. We derive a uniform algorithm based on this idea for incomplete data case and present the convergence and optimality properties. Such an algorithm is suitable trivially under complete observations.
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    An Introduction to Belief Networks
    (1999) Li, Hongjun; Baras, John S.; ISR; CSHCN
    Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in the late 1970s to model the distributed processing in reading comprehension. Since then they have attracted much attention and have become popular within the AI probability and uncertainty community. As a natural and efficient model for humans' inferential reasoning, belief networks have emerged as the general knowledge representation scheme under uncertainty.

    In this report, we first introduce belief networks in the light of knowledge representation under uncertainty, then in the remainingsections we give the descriptions of the semantics, inference mechanisms and some issues related to learning belief networks, respectively. This report is not intended to be a tutorial for beginners. Rather it aims to point out some important aspects of belief networks and summarize some important algorithms.

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    An Automated, Distributed, Intelligent Fault Management System for Communication Networks
    (1999) Li, Hongjun; Baras, John S.; Mykoniatis, George; ISR; CSHCN
    In this paper we present a Distributed Intelligent Fault Management (DIFM) system for communication networks. The overall architecture of the proposed system is based on a distributed, cooperative, multi-agent paradigm, with probabilistic networks as the framework for knowledge representation and evidence inferencing. We adopt the management by delegation paradigm for network monitoring and integrate both hard and soft faults.
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    Integrated, Distributed Fault Management for Communication Networks
    (1998) Baras, John S.; Li, Hongjun; Mykoniatis, G.; Baras, John S.; ISR; CSHCN
    This report describes an integrated, distributed fault management (IDFM) system for communication networks. The architecture is based on a distributed intelligent agent paradigm, with probabilistic networks as the framework for knowledge representation and evidence inferencing. A static strategy for generating the suggestive test sequence is proposed, based on which a heuristic dynamic strategy is initiated. Another dynamic strategy, formulated as a Markov decision problem, is also provided. To solve this problem, reinforcement learning techniques are investigated.