Report on the Twenty-Second International Conference on Case-Based Reasoning

AI Magazine ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 88-89
Author(s):  
Derek Bridge ◽  
Luc Lamontagne ◽  
Enric Plaza
Keyword(s):  
Artificial Intelligence ◽  
Annual Meeting ◽  
Case Based Reasoning ◽  
International Meeting ◽  
European Conference ◽  
International Conference ◽  
Case Based ◽  
Second International

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010.

scholarly journals Report on the 21st International Conference on Case-Based Reasoning

AI Magazine ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 119-120
Author(s):  
Santiago Ontanon ◽  
Sarah Jane Delany ◽  
William E. Cheetham
Keyword(s):  
Artificial Intelligence ◽  
Annual Meeting ◽  
Case Based Reasoning ◽  
International Meeting ◽  
International Conference ◽  
Case Based ◽  
Saratoga Springs

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the twenty-first International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in Case-Based Reasoning (CBR), was held in July 2013 in Saratoga Springs, NY. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. This year ICCBR featured the Industry Day, the fifth annual Doctoral Consortium and three workshops.

scholarly journals Report on the 24th International Conference on Case-Based Reasoning Research and Development (ICCBR-2016)

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 89-90
Author(s):  
Belen Diaz-Agudo ◽  
Ashok K. Goel
Keyword(s):  
Research And Development ◽  
Annual Meeting ◽  
Case Based Reasoning ◽  
Georgia Institute ◽  
International Conference ◽  
Institute Of Technology ◽  
The University ◽  
Case Based

The Twenty-Fourth International Conference on Case-Based Reasoning Research and Development, ICCBR 2016, was held October 31st to November 2nd, in Atlanta, Georgia, USA, colocated with the Fourth International Conference on Design and Creativity. ICCBR is the premier, annual meeting of the CBR community and the leading international conference on this topic. The theme for the ICCBR 2016 was Creativity. The conference chair was Ashok K. Goel, from Georgia Institute of Technology, USA, and the program cochairs were Belen Diaz-Agudo from Complutense University, Spain, and Thomas Roth-Berghofer from the University of West London, UK.

scholarly journals The 27th International Conference on Case-Based Reasoning

AI Magazine ◽  
2020 ◽  
Vol 41 (1) ◽  
pp. 101-102
Author(s):  
Klaus-Dieter Althoff ◽  
Kerstin Bach ◽  
Ralph Bergmann ◽  
Cindy Marling
Keyword(s):  
Artificial Intelligence ◽  
Case Based Reasoning ◽  
International Conference ◽  
Technical Paper ◽  
Explainable Artificial Intelligence ◽  
Case Based

The 27th International Conference on Case-Based Reasoning was held September 8–12, 2019, in Otzenhausen, Germany. The theme of the conference was explainable artificial intelligence. The conference featured four invited talks, an invited panel, four workshops, a doctoral consortium, four technical paper sessions, and a poster and system demonstration session. This report summarizes conference highlights.

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.

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.

Cyber Physical Control Systems

2015 ◽  
pp. 263-285
Keyword(s):  
Artificial Intelligence ◽  
Control Systems ◽  
Intelligent Systems ◽  
Control Strategies ◽  
Case Based Reasoning ◽  
Physical Control ◽  
Closed Loop Systems ◽  
Intelligence Models ◽  
Algorithmic Design ◽  
Case Based

The focus of algorithmic design is to solve composite problems. Intelligent systems use intellectual concepts like evolutionary computation, artificial neural networks, fuzzy systems, and swarm intelligence to process natural intelligence models. Artificial intelligence is used as a part of intelligent systems to perform logic- and case-based reasoning. Systems like mechanical and electrical support systems are operated by utilizing Supervisory Control and Data Acquisition (SCADA) systems. These systems cannot accomplish their purpose, provided the control system deals with the reliability of it. In CPSs, dimensions of physical processes are taken by sensors and are processed in cyber subsystems to drive the actuators that affect the physical processors. CPSs are closed-loop systems. The adaptation and the prediction are the properties to be followed by the control strategies that are implemented in cyber subsystems. This chapter explores cyber physical control systems.

Individual Prognosis of Diabetes Long-term Risks: A CBR Approach

2001 ◽  
Vol 40 (01) ◽  
pp. 46-51 ◽  
Author(s):  
A. Palaudàries ◽  
E. Plaza ◽  
E. Armengol
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Complication ◽  
Diabetic Patients ◽  
Case Based Reasoning ◽  
Artificial Intelligence Technique ◽  
Past Experiences ◽  
Risk Pattern ◽  
Case Based ◽  
Intelligence Technique

AbstractWe present DIRAS, an application to support physicians in determining the risk of complications for individual diabetic patients. The risk pattern of each diabetic patient is obtained using a Case-based Reasoning method called LID. Case-based Reasoning is an Artificial Intelligence technique based on solving new situations according to past experiences. For each patient, the LID method determines the risk of each diabetic complication according to the risk of already diagnosed patients. In addition, LID builds a description that can be viewed as an explanation of the obtained risk.

A Case-Based Reasoning Approach for the Cybersecurity Incident Recording and Resolution

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1607-1627
Author(s):  
Raul Ceretta Nunes ◽  
Marcelo Colomé ◽  
Fabio André Barcelos ◽  
Marcelo Garbin ◽  
Gustavo Bathu Paulus ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Case Based Reasoning ◽  
Intelligent Computing ◽  
Resolution System ◽  
Concrete Problem ◽  
Different Types ◽  
Practice Knowledge ◽  
Resolution Data ◽  
Case Based

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems that already took place. In practice, knowledge regarding cybersecurity resolution data and procedures is being under-used in the development of intelligent cybersecurity systems, sometimes even lost and not used at all. In this context, this work proposes the Case-based Cybersecurity Incident Resolution System (CCIRS), a system that implements an approach to integrate case-based reasoning (CBR) techniques and the IODEF standard in order to retain concrete problem-solving experiences of cybersecurity incident resolution to be reused in the resolution of new incidents. Different types of experimental results so far obtained with the CCIRS show that information security knowledge can be retained with our approach in a reusable memory improving the resolution of new cybersecurity problems.

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