University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Bayesian Artificial Intelligence for Tackling Uncertainty in Self-Adaptive Systems: The Case of Dynamic Decision Networks

Bayesian Artificial Intelligence for Tackling Uncertainty in Self-Adaptive Systems: The Case of Dynamic Decision Networks

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If you have a question about this talk, please contact Leandro Minku.

Note: Topic of Artificial Intelligence and Software Engineering. Host: Leandro Minku

In recent years, there has been a growing interest towards the application of artificial intelligence approaches in software engineering (SE) processes. In the specific area of SE for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and AI. However, just few significant results have been published. We report and discuss our own experience using Dynamic Decision Networks (DDNs) to model and support decision-making in SASs while explicitly taking into account uncertainty. In this session we talk about the application of our DDN -based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision. We also discuss the ongoing work on a Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behavior. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a “surprising” event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening.

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