Exploration vs. Exploitation in Case-Base Maintenance: Leveraging Competence-Based Deletion with Ghost Cases

2018 ◽  
pp. 202-218 ◽  
Author(s):  
David Leake ◽  
Brian Schack
Keyword(s):  
Case Base

A Novel Discovery Technique on Attribute Weight of Engine CBR Design System

2010 ◽  
Vol 97-101 ◽  
pp. 3714-3717 ◽  
Author(s):  
Wei Yan ◽  
Qi Gao ◽  
Zheng Gang Liu ◽  
Shan Hui Zhang ◽  
Yu Ping Hu
Keyword(s):  
Evolutionary Programming ◽  
Programming Algorithm ◽  
Design System ◽  
Reference Case ◽  
Attribute Weight ◽  
Case Base ◽  
Mutation Strategies ◽  
Group Self ◽  
Gauss Operator ◽  
Self Adaptive

An improved multi-group self-adaptive evolutionary programming Algorithm is used to get adapt attribute weight for CBR system. Firstly, this paper analyses the adaptability function based on reference case base REF and testing case base TEST, develops a novel Bi-group self-adaptive evolutionary programming that overcome the lack of conventional evolutionary programming. In this Novel algorithm, evolution of Cauchy operator and Gauss operator are parallel performed with different mutation strategies, and the Gauss operator owns the ability of self-adaptation according to the variation of adaptability function. Information is exchanged when sub-groups are reorganized. Experiment results prove the validity of self-adaptive Algorithm and CBR design system is used successfully in engine design process.

Case Base Elicitation for a Context-Aware Recommender System

2018 ◽  
pp. 170-185
Author(s):  
Jose Luis Jorro-Aragoneses ◽  
Guillermo Jimenez-Díaz ◽  
Juan Antonio Recio-García ◽  
Belén Díaz-Agudo
Keyword(s):  
Recommender System ◽  
Context Aware ◽  
Case Base

iCaseViz: Learning Case Similarities through Interaction with a Case Base Visualizer

2013 ◽  
pp. 203-217
Author(s):  
Debarun Kar ◽  
Anand Kumar ◽  
Sutanu Chakraborti ◽  
Balaraman Ravindran
Keyword(s):  
Case Base

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

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.

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