The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지

Probability-Based Context-Generation Model with Situation Propagation Network

상황 전파 네트워크를 이용한 확률기반 상황생성 모델

  • 천성표 (부산대학교 BK21 영상IT 산학공동 사업단) ;
  • 김성신 (부산대학교 전자전기공학부)
  • Received : 2009.01.21
  • Accepted : 2009.02.17
  • Published : 2009.02.27
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Abstract

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

Keywords

References

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