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Probability-based Context-generation Model with Situation Propagation Network

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  • URLhttps://db.koreascholar.com/Article/Detail/1042
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로봇학회논문지 (The Journal of Korea Robotics Society)
한국로봇학회 (Korea Robotics Society)
초록

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 probability-based model with respect to performance, reduction of ambiguity, and confliction.

저자
  • 천성표 | Cheon Seong-Pyo
  • 김성신 | Kim Sungshin