정보과학회 논문지 (Journal of KIISE)

  • 제43권7호
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  • Pages.801-811
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  • 2016
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  • 2383-630X(pISSN)
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  • 2383-6296(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지
DOI QR코드

DOI QR Code

환경정보를 고려한 자가적응형 시스템을 위한 동적 의사결정 기술

Dynamic Decision Making for Self-Adaptive Systems Considering Environment Information

  • 김미수 (성균관대학교 전자전기컴퓨터공학과) ;
  • 정호현 (성균관대학교 전자전기컴퓨터공학과) ;
  • 이은석 (성균관대학교 소프트웨어대학)
  • 투고 : 2016.03.11
  • 심사 : 2016.04.27
  • 발행 : 2016.07.15
⟨ 이전 논문 다음 논문 ⟩

초록

자가적응형 시스템은 다양한 환경에서 시스템의 목표를 달성할 수 있도록 스스로 행동이나 세부 목표 등을 바꿀 수 있는 능력이 요구된다. 기존의 관련 연구들은 목표의 만족도를 평가하고 예측하기 위한 유틸리티 함수 및 각 목표를 만족시키기 위한 정책들을 설계 시에 결정하여 런타임의 다양한 상황에 적응한다. 그러나 설계 시점에서 시스템이 배치될 환경에 대해 완벽히 예측할 수 없기 때문에 적절한 적응성을 보장하기 어렵다. 본 논문에서는 상기의 문제에 대응하기 위해 시스템 상황 정보를 나타내는 환경정보와 목표모델을 동적 결정 네트워크로 설계하고, 이에 실시간 환경정보를 반영하여 목표의 만족도를 평가하고 예측한다. 또한 실시간 정보를 통한 설계된 모델의 갱신 기법과, 해당 기법에 기반하여 동적 환경에 적절히 대응 가능한 자가적응형 시스템을 위한 동적 의사결정 기술을 제시한다. 제안 방법론의 검증을 위해 로보코드에 적용하여 그 유효성을 확인하였다.

Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단, 정보통신기술진흥센터

참고문헌

  1. M. Salehie et al., "Self-adaptive software: Landscape and research challenges," ACM Transactions on Autonomous and Adaptive Systems, Vol. 4, No. 2, pp. 1-42, 2009.
  2. R. D. Lemos et al., "Software engineering for selfadaptive systems: A second research roadmap," Software Engineering for Self-Adaptive Systems II, pp. 1-32, 2013.
  3. L. Baresi et al., "Fuzzy goals for requirementsdriven adaptation," Proc. of the 18th International IEEE Requirements Engineering Conference (RE 2010), pp. 125-134, 2010.
  4. L. Baresi et al., "Adaptive goals for self-adaptive service compositions," Proc. of the 2010 IEEE international conference on web services (ICSW 2010), pp. 353-360, 2010.
  5. B. Chen et al., "Requirements-driven self-optimization of composite services using feedback control," IEEE Transactions on Services Computing, Vol. 8, No. 1, pp. 107-120, 2015. https://doi.org/10.1109/TSC.2014.2298866
  6. M. Morandini et al., "Towards goal-oriented development of self-adaptive systems," Proc. of the international workshop on Software engineering for adaptive and self-managing systems (SEAMS 2008), pp. 9-16, 2008.
  7. K. Welsh et al., "Self-explanation in adaptive systems based on runtime goal-based models," Transactions on Computational Collective Intelligence XVI, pp. 122-145, 2014.
  8. S.J. Russell et al., Artificial intelligence: A modern approach, 2nd ed., Prentice Hall series in artificial intelligence, Prentice Hall, 2003.
  9. N. Bencomo et al., "Supporting decision-making forself-adaptive systems: from goal models to dynamicdecision networks," Requirements Engineering: Foundationfor Software Quality, pp. 221-236, 2013.
  10. M. Kim et al., "An extended dynamic decision network," Journal of Korean Institute of Information Scientists and Engineers (KIISE): Software and Applications, Vol. 42, No. 7, pp. 889-900, 2015. (in Korean)
  11. IBM. ROBOCODE [Online]. Available: http://robocode.sourceforge.net/(downloaded 2015, June. 10)
  12. P. C. G. Costa, "The fighter aircraft's auto-defense management problem: a dynamic decision network approach," Master's thesis, George Mason University, 1999.
  13. J. L. Molina et al., "Dynamic Bayesian networks as a decision support tool for assessing climate change impacts on highly stressed groundwater systems," Journal of Hydrology, Vol. 479, pp. 113-129, 2013. https://doi.org/10.1016/j.jhydrol.2012.11.038
  14. N. Z. Naqvi et al., "The right thing to do: Automatingsupport for assisted living with dynamicdecision networks," Proc. of the 10th international conference on Ubiquitous Intelligence and Computing and 10th international conference on Autonomic and Trusted Computing (UIC-ATC), pp. 262-269, 2013.
  15. S. Piao et al., "Problem Localization using ProbabilisticDependency Analysis for Automated System Management in Ubiquitous Computing," Internet Research, Vol. 19, No. 1, pp. 136-152, 2009. https://doi.org/10.1108/10662240910952319
  16. B. W. Boehm, "Value-based software engineering: Overview and agenda," Value-based software engineering, pp. 3-14, 2006.
  17. T. T. Womg, "Generalized Dirichlet distribution in Bayesian analysis," Applied Mathematics and Computation, Vol. 97, No. 2, pp. 165-181, 1998. https://doi.org/10.1016/S0096-3003(97)10140-0
  18. N. S. Altman, "An introduction to kernel and nearest neighbor nonparametric regression," The American Statistician, Vol. 46, No. 3, pp. 175-185, 1992.
  19. IBM. RobotSearch [Online]. Available: http://robocoderepository.com/(downloaded 2015, June. 10)
  20. CISIAD. OpenMarkov [Online]. Available: http://www.openmarkov.org/(downloaded 2015, Jun. 10)