On the role of abstraction in case-based reasoning

1996 ◽  
pp. 28-43 ◽  
Author(s):  
Ralph Bergmann ◽  
Wolfgang Wilke
Keyword(s):  
Case Based Reasoning ◽  
Case Based

The Role of Professional Knowledge in Case-Based Reasoning in Practical Ethics

2015 ◽  
Vol 21 (3) ◽  
pp. 767-787 ◽  
Author(s):  
Rosa Lynn Pinkus ◽  
Claire Gloeckner ◽  
Angela Fortunato
Keyword(s):  
Professional Knowledge ◽  
Case Based Reasoning ◽  
Practical Ethics ◽  
Case Based

scholarly journals Retrieval, reuse, revision and retention in case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 215-240 ◽  
Author(s):  
RAMON LOPEZ DE MANTARAS ◽  
DAVID MCSHERRY ◽  
DEREK BRIDGE ◽  
DAVID LEAKE ◽  
BARRY SMYTH ◽  
...  
Keyword(s):  
Problem Solving ◽  
Analogical Reasoning ◽  
Case Based Reasoning ◽  
The Past ◽  
Future Problem ◽  
Considerable Success ◽  
Representative Selection ◽  
Case Based ◽  
Selection Of

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

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