Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Selective Inference in Modular Bayesian Networks for Lightweight Context Inference in Cell Phones

휴대폰에서의 경량 상황추론을 위한 모듈형 베이지안 네트워크의 선택적 추론

  • 이승현 (연세대학교 컴퓨터과학과) ;
  • 임성수 (연세대학교 컴퓨터과학과) ;
  • 조성배 (연세대학교 컴퓨터과학과)
  • Received : 2010.04.13
  • Accepted : 2010.09.02
  • Published : 2010.10.15
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Abstract

Log data collected from mobile devices contain diverse and meaningful personal information. However, it is not easy to implement a context-aware mobile agent using this personal information due to the inherent limitation in mobile platform such as memory capacity, computation power and its difficulty of analysis of the data. We propose a method of selective inference for modular Bayesian Network for context-aware mobile agent with effectiveness and reliability. Each BN module performs inference only when it can change the result by comparing to the history module which contains evidences and posterior probability, and gets results effectively using a method of influence score of the modules. We adopt memory decay theory and virtual linking method for the evaluation of the reliability and conservation of casual relationship between BN modules, respectively. Finally, we confirm the usefulness of the proposed method by several experiments on mobile phones.

모바일 기기에서 얻을 수 있는 로그 데이터는 다수의 유의미한 정보를 담고 있다. 그러나 모바일 기기의 연산능력 제약과 정보 분석 자체의 어려움 등으로 상황정보를 활용한 모바일 에이전트의 구현이 쉽지 않다. 본 논문에서는 제한적인 모바일 플랫폼에서 효율적인 상황인지를 위한 베이지안 네트워크 용용 기법을 제안한다. 베이지안 네트워크는 다수의 세부 모듈로 구성되며, 모듈간 인과성은 가상증거를 통해 보존된다. 각 모듈은 이전 증거값과 추론결과를 저장하고, 현재 증거값과 비교하여 전체 네트워크에 변화를 주는 경우에만 선택적으로 추론을 수행한다. 다양한 수집 주기의 모바일 데이터를 이용한 추론결과의 신뢰성을 높이기 위해 기억감소함수를 이용하여 결과를 보정하는 방법을 살펴본다. 마지막으로 실제 모바일 환경에서의 실험을 통해 제안하는 방법의 유용성을 확인한다.

Keywords

References

  1. R. Vertegaal, C. Dickie and C. Sohn, "Designing Attentive Cell Phone using Wearable Eyecontact Sensors," CHI Extended Abstracts on Human Factors in Computing Systems, pp.646-647, 2002.
  2. N. Eagle, "Machine Perception and Learning of Complex Social Systems," Ph.D. Thesis, Massachusetts Institute of Technology, 2005.
  3. A. Krause, A. Smailagic, and D. P. Siewiorek, "Context-aware Mobile Computing: Learning Context- dependent Personal Preferences from a Wearable Sensor Array," IEEE Trans. on Mobile Computing, vol.5, no.2, pp.113-127, 2006. https://doi.org/10.1109/TMC.2006.18
  4. G. F. Cooper, "The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks," Journal of Artificial Intelligence, vol.42, pp.393-405, 1990. https://doi.org/10.1016/0004-3702(90)90060-D
  5. K.-S. Hwang, S.-B. Cho, "Landmark Detection from Mobile Life Log using a Modular Bayesian Network Model," Expert Systems with Applications, vol.36, pp.12065-12076, 2009. https://doi.org/10.1016/j.eswa.2009.03.002
  6. M. Marengoni, A. Hanson, S. Zilberstein and E. Riseman, "Decision Making and Uncertainty Management in a 3D Reconstruction System," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.25, no.7, pp.852-858, 2003. https://doi.org/10.1109/TPAMI.2003.1206514
  7. B. Brandherm and T. Schwartz, "Geo Referenced Dynamic Bayesian Networks for User Positioning on Mobile Systems," Lecture Notes in Computer Science, vol.3479, pp.223-234, 2005.
  8. Y. Xiang and F. V. Jensen, "Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests," Studies in Fuzziness and Soft Computing, vol.213, pp.175-192, 2007.
  9. A. Brogini and D. Slanzi, "On using Bayesian Networks for Complexity Reduction in Decision Trees," Statistical Methods and Applications, vol. 19, no.1, pp.127-139, 2009.
  10. U. Kjaerulff, "Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependences," Proceedings on Uncertainty in Artificial Intelligence, pp.374-382, 1994.
  11. F. Bacchus, S. Dalmao, and T. Pitassi, "Algorithms and Complexity Results for #SAT and Bayesian Inference," IEEE Symposium on Foundations of Computer Science, pp.340-355, 2003.
  12. G. Pavlin, et al., "A Multi Agent Systems Approach to Distributed Bayesian Information Fusion," Information Fusion, vol.11, no.3, pp.267-282, 2009.
  13. K. B. Korb., Bayesian Artificial Intelligence, Chapman & Hall/CRC, pp.62-68, 2004.
  14. V. K. Namasivayam and V. K. Prasanna, "Salable Parallel Implementation of Exact Inference in Bayesian Networks," International Conference on Parallel and Distributed Systems, vol.1, pp.143-150, 2006.
  15. C. Huang, and A. Darwiche, "Inference in Belief Networks: A Procedural Guide," International Journal of Approximate Reasoning, vol.15, no.3, pp.225-263. 1996. https://doi.org/10.1016/S0888-613X(96)00069-2