浙江大學(xué)研究生人工智能引論課件運(yùn)功第五講d-s證據(jù)理(已修改)
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【正文】 課件》人工智能引論《浙江大學(xué)研究生徐從富 (Congfu Xu) PhD, Associate Professor Email: Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou 310027, . ChinaMarch 10, 2023第一稿September 22, 2023第五次修改稿第五講 DS證據(jù)理論(Chapter5 DS Evidential Theory )Outlinen 本章的主要參考文獻(xiàn)n 證據(jù)理論的發(fā)展簡況n 經(jīng)典證據(jù)理論n 關(guān)于證據(jù)理論的理論模型解釋n 基于 DS理論的不確定性推理n 證據(jù)理論的實(shí)現(xiàn)途徑n 計(jì)算舉例[1] Dempster, A. P. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 1967, 38(2): 325339. 【 提出證據(jù)理論的第一篇文獻(xiàn)】[2] Dempster, A. P. Generalization of Bayesian Inference. Journal of the Royal Statistical Society. Series B 30, 1968:205247.[3] Shafer, G. A Mathematical Theory of Evidence. Princeton University Press, 1976. 【 證據(jù)理論的第一本專著，標(biāo)志其正式成為一門理論】[4] Bart, J. A. Computational methods for a mathematical theory of evidence. In: Proceedings of 7th International Joint Conference on Artificial Intelligence(IJCAI81), Vancouver, B. C., Canada, Vol. II, 1981: 868875. 【 第一篇將證據(jù)理論引入 AI領(lǐng)域的標(biāo)志性論文】本章的主要參考文獻(xiàn)[5] Zadeh, L. A. Review of Shafer’s a mathematical theory of evidence. AI Magazine, 1984, 5:8183. 【 對證據(jù)理論進(jìn)行質(zhì)疑的經(jīng)典文獻(xiàn)之一】[6] Shafer, G. Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning, 1990, 4: 323362. [7] Shafer, G. Rejoinder to ments on “Perspectives on the theory and practice of belief functions”. International Journal of Approximate Reasoning, 1992, 6: 445480. [8] Voorbraak, F. On the justification of Dempster’s rule of bination. Artificial Intelligence, 1991, 48:171197.[9] Smets, P. The bination of evidence in the transferable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(5): 447458. [10] Smets, P, and Kennes, R. The transferable belief model. Artificial Intelligence, 1994, 66: 191234. 本章的主要參考文獻(xiàn) （續(xù) 1）[11] Voobraak, F. A putationally efficient approximation of DempsterShafer theory. International Journal of ManMachine Study, 1989, 30: 525536. [12] Dubois, D, Prade, H. Consonant approximations of belief functions. International Journal of Approximate Reasoning, 1990, 4: 279283. [13] Tessem, B. Approximations for efficient putation in the theory of evidence. Artificial Intelligence, 1993, 61:315329. 【 注：文獻(xiàn) 1012均為證據(jù)理論近似計(jì)算方法】[14] Simard, M. A., et al. Data fusion of multiple sensors attribute information for target identity estimation using a DempsterShafer evidential bination algorithm. In: Proceedings of SPIEInternational Society for Optical Engineering, 1996, : 577588. 【 提出了一種實(shí)現(xiàn)證據(jù)理論的 “修剪算法 ”】本章的主要參考文獻(xiàn) （續(xù) 2）[15] Josang, A. The consensus operator for bining beliefs. Artificial Intelligence, 2023, 141(12): 157170. [16] Yang, JianBo, Xu, DongLing. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Transaction on Systems, Man, and Cyberics – Part A: Systems and Humans, 2023, 32(3): 289304. [17] Yaghlane, B. B., et al. Belief function independence: I. The marginal case. Internati
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