Porovnat metody

	Teorie důkazů Dempstera-Shafera	Případové usuzování (CBR)
Obor	Soft computing	Soft computing
Rodina	Machine learning	Machine learning
Rok vzniku	1976	1994
Tvůrce	Arthur P. Dempster & Glenn Shafer	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
Typ	Uncertainty calculus for combining evidence	Experience-based (analogical) problem solving
Původní zdroj	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.
Další názvy	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Příbuzné	4	2
Shrnutí	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.