Uncertainty in Artificial Intelligence
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Belief-Kinematics Jeffrey‚??s Rules in the Theory of Evidence
Chunlai Zhou, Mingyue Wang, Biao Qin
This paper studies the problem of revising belief- s using uncertain evidence in a framework where beliefs are represented by a belief function. We introduce two new Jeffrey‚??s rules for the revi- sion based on two forms of belief kinematics, an evidence-theoretic counterpart of probability kinematics. Furthermore, we provide two dis- tance measures for belief functions and show that the two belief kinematics are optimal in the sense that they minimize their corresponding distance measures.
Pages: 917-926
PS Link:
PDF Link: /papers/14/p917-zhou.pdf
AUTHOR = "Chunlai Zhou and Mingyue Wang and Biao Qin",
TITLE = "Belief-Kinematics Jeffrey‚??s Rules in the Theory of Evidence",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "917--926"

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