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Computer Sciences

2010

Bayesian networks

Articles 1 - 4 of 4

Full-Text Articles in Engineering

Towards Software Health Management With Bayesian Networks, Johann Schumann, Ole J. Mengshoel, Ashok Srivastava, Adnan Darwiche Oct 2010

Towards Software Health Management With Bayesian Networks, Johann Schumann, Ole J. Mengshoel, Ashok Srivastava, Adnan Darwiche

Ole J Mengshoel

More and more systems (e.g., aircraft, machinery, cars) rely heavily on software, which performs safety-critical operations. Assuring software safety though traditional V&V has become a tremendous, if not impossible task, given the growing size and complexity of the software. We propose that iSWHM (Integrated SoftWare Health Management) can increase safety and reliability of high-assurance software systems. iSWHM uses advanced techniques from the area of system health management in order to continuously monitor the behavior of the software during operation, quickly detect anomalies and perform automatic and reliable root-cause analysis, while not replacing traditional V&V. Information provided by the iSWHM system …

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Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan Jun 2010

Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan

Ole J Mengshoel

Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will remain in the population (replacement phase). The present work focuses on the replacement phase of crowding, which usually has been carried out by one of the following three approaches: Deterministic, Probabilistic, and Simulated Annealing. These approaches present some limitations regarding the way replacement is conducted. On the one hand, the first two apply the same …

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Understanding The Scalability Of Bayesian Network Inference Using Clique Tree Growth Curves, Ole J. Mengshoel Apr 2010

Understanding The Scalability Of Bayesian Network Inference Using Clique Tree Growth Curves, Ole J. Mengshoel

Ole J Mengshoel

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a BN, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of …

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Portfolios In Stochastic Local Search: Efficiently Computing Most Probable Explanations In Bayesian Networks, Ole J. Mengshoel, D Roth, D Wilkins Feb 2010

Portfolios In Stochastic Local Search: Efficiently Computing Most Probable Explanations In Bayesian Networks, Ole J. Mengshoel, D Roth, D Wilkins

Ole J Mengshoel

Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) …

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