A Knowledge Management System Using Bayesian Networks
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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The Bayesian networks support resource allocation in software project and also help in analyzing trade-offs among resources. The model predicts the probability distribution of every variable given incomplete data. Even though the Bayesian networks conveniently facilitate scenario-based analysis, they do not support finding an optimal solution in multi-criteria decision making. This paper proposes extending the Bayesian networks into the decision networks to optimize an organizational target and to handle the multi-criteria environment of software project management. Specifically, the decision networks are used to find an optimal set of software activities under constraints of software cost and quality. The preliminary results demonstrate that the Bayesian networks can be easily extended into the decision networks, which then allow for optimization. The proposed methodology provides a flexible process for utilizing the encoded knowledge within the Bayesian networks to facilitate decision making, which could be applicable in other domains of problems.