Compara mètodes

	Raonament basat en casos (CBR)	Mapes Cognitius Difusos (FCM)
Camp	Computació tova	Computació tova
Família	Machine learning	Process / pipeline
Any d'origen	1994	1986
Autor original	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)	Bart Kosko
Tipus	Experience-based (analogical) problem solving	Fuzzy causal/feedback network for scenario analysis
Font seminal	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.	Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.
Àlies	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar
Relacionats	2	4
Resum	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.	A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.