Bandingkan metode

	Penalaran Berbasis Kasus (PBK)	Komputasi Granular (Granulasi Informasi)
Bidang	Komputasi Lunak	Komputasi Lunak
Keluarga	Machine learning	Machine learning
Tahun asal	1994	1997
Pencetus	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao
Tipe	Experience-based (analogical) problem solving	Framework for multi-granularity information processing
Sumber perintis	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.	Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.
Alias	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama
Terkait	2	3
Ringkasan	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.	Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.