Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks

Nelly Bencomo, Amel Belaggoun, Valerie Issarny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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. This paper briefly studies uncertainty in SASs and surveys techniques that have been developed to engineer SASs in order to tackle uncertainty. In particular, we highlight techniques that use AI concepts. We also 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. We think that Bayesian inference, and specifically DDNs, provide a useful formalism to engineer systems that dynamically adapt themselves at runtime as more information about the environment and the execution context is discovered during execution. We also discuss partial results, challenges and future research avenues.

Original languageEnglish
Title of host publication2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - Proceedings
Pages7-13
Number of pages7
DOIs
Publication statusPublished - 4 Nov 2013
Event2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - San Francisco, CA, United States
Duration: 25 May 201326 May 2013

Conference

Conference2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013
CountryUnited States
CitySan Francisco, CA
Period25/05/1326/05/13

Fingerprint

Adaptive systems
Artificial intelligence
Software engineering
Engineers
Decision making
Uncertainty

Keywords

  • Bayesian inference
  • bayesian networks
  • dynamic-decision net-works
  • self-adaptive systems
  • uncertainty modeling

Cite this

Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. In 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - Proceedings (pp. 7-13). [6615198] https://doi.org/10.1109/RAISE.2013.6615198
Bencomo, Nelly ; Belaggoun, Amel ; Issarny, Valerie. / Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems : The case of dynamic decision networks. 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - Proceedings. 2013. pp. 7-13
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Bencomo, N, Belaggoun, A & Issarny, V 2013, Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. in 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - Proceedings., 6615198, pp. 7-13, 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013, San Francisco, CA, United States, 25/05/13. https://doi.org/10.1109/RAISE.2013.6615198

Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems : The case of dynamic decision networks. / Bencomo, Nelly; Belaggoun, Amel; Issarny, Valerie.

2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - Proceedings. 2013. p. 7-13 6615198.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Bencomo N, Belaggoun A, Issarny V. Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. In 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013 - Proceedings. 2013. p. 7-13. 6615198 https://doi.org/10.1109/RAISE.2013.6615198