Case-Based, Model-Based, and Explanation-Based Styles of Reasoning in Foreign Policy

1994 ◽  
Vol 38 (1) ◽  
pp. 61 ◽  
Author(s):  
Donald A. Sylvan ◽  
Thomas M. Ostrom ◽  
Katherine Gannon
Keyword(s):  
Foreign Policy ◽  
Model Based ◽  
Case Based ◽  
Styles Of Reasoning

Survey on structure-based case retrieval

1997 ◽  
Vol 12 (01) ◽  
pp. 41-58 ◽  
Author(s):  
FRIEDRICH GEBHARDT
Keyword(s):  
Structural Properties ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Model Based ◽  
Main Components ◽  
Case Based

The main components of case-based reasoning are case retrieval and case reuse. While case retrieval mostly uses attribute comparisons, many other possibilities exist. The case similarity concepts described in the literature that are based on more elaborate structural properties are classified here into five groups: restricted geometric relationships; graphs; semantic nets; model-based similarities; hierarchically structured similarities. Some general topics conclude this survey on structure-based case retrieval methods and systems.

scholarly journals Maintenance of Case Bases: Current Algorithms after Fifty Years

2018 ◽  
Author(s):  
Jose M. Juarez ◽  
Susan Craw ◽  
J. Ricardo Lopez-Delgado ◽  
Manuel Campos
Keyword(s):  
Big Data ◽  
50Th Anniversary ◽  
Case Based Reasoning ◽  
Changing Environments ◽  
Model Based ◽  
New Knowledge ◽  
Case Base ◽  
Case Based

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from model-based systems since it can learn incrementally as new data is available, storing new cases in its case-base. This means that it can benefit from readily available new data, but also case-base maintenance (CBM) is essential to manage the cases, deleting and compacting the case-base. In the 50th anniversary of CNN (considered the first CBM algorithm), new CBM methods are proposed to deal with the new requirements of Big Data scenarios. In this paper, we present an accessible historic perspective of CBM and we classify and analyse the most recent approaches to deal with these requirements.

scholarly journals From design experiences to generic mechanisms: Model-based learning in analogical design

1996 ◽  
Vol 10 (2) ◽  
pp. 131-136 ◽  
Author(s):  
Sambasiva R. Bhatta ◽  
Ashok K. Goel
Keyword(s):  
Analogical Reasoning ◽  
Feedback Mechanism ◽  
Short Paper ◽  
Creative Design ◽  
Learning Method ◽  
Model Based ◽  
Cross Domain ◽  
Behavior Function ◽  
Function Models ◽  
Case Based

AbstractAnalogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.

Case-based reasoning: a categorized bibliography

1994 ◽  
Vol 9 (4) ◽  
pp. 355-381 ◽  
Author(s):  
Farhi Marir ◽  
Ian Watson
Keyword(s):  
Expert Systems ◽  
Case Based Reasoning ◽  
Human Reasoning ◽  
Model Based ◽  
The Subject ◽  
Case Based

Case-Based Reasoning (CBR) is a fresh reasoning paradigm for the design of expert systems in domains that may not be appropriate for other reasoning paradigms such as model-based reasoning. As a result of this, and because of its resemblance to human reasoning, CBR has attracted increasing interest both from those experienced in developing expert systems and from novices. Although CBR is a relatively new discipline, there are an increasing number of papers and books being published on the subject. In this context, this bibliographic categorization is an accompanying paper to a review of CBR by the same authors. The objective of this paper is to help researchers quickly identify relevant references.

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