Methoden vergleichen

	Dempster-Shafer-Theorie der Evidenz	Fallbasiertes Schließen (Case-Based Reasoning, CBR)
Fachgebiet	Soft Computing	Soft Computing
Familie	Machine learning	Machine learning
Entstehungsjahr	1976	1994
Urheber	Arthur P. Dempster & Glenn Shafer	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
Typ	Uncertainty calculus for combining evidence	Experience-based (analogical) problem solving
Wegweisende Quelle	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.
Aliasnamen	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Verwandt	4	2
Zusammenfassung	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.