مقایسهٔ روش‌ها

	نظریه شواهد دمپستر-شافر	استدلال مبتنی بر مورد (CBR)
حوزه	محاسبات نرم	محاسبات نرم
خانواده	Machine learning	Machine learning
سال پیدایش	1976	1994
پدیدآور	Arthur P. Dempster & Glenn Shafer	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
نوع	Uncertainty calculus for combining evidence	Experience-based (analogical) problem solving
منبع بنیادین	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
نام‌های دیگر	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
مرتبط	4	2
خلاصه	Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.	Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.