A world full of surprises: Bayesian theory of surprise to quantify degrees of uncertainty

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In the specific area of software engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and artificial intelligence (AI). However, just few significant results have been published so far. In this paper, we propose a novel and formal 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. In this paper, we discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian dynamic decision networks.

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Publication date2014
Publication titleCompanion : proceedings of the 36th International Conference on Software Engineering
Place of PublicationNew York, NY (US)
Number of pages4
ISBN (Print)978-1-4503-2768-8
Original languageEnglish
Event36th International Conference on Software Engineering - Hyderabad, India


Conference36th International Conference on Software Engineering
Abbreviated titleICSE 2014


  • Bayesian networks, Bayesian surprise, self-adaptation, uncertainty


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