Supporting modeling and problem solving from precedent experiences: the role of workflows and case-based reasoning

2005 ◽  
Vol 20 (6) ◽  
pp. 689-704 ◽  
Author(s):  
Daniel S. Kaster ◽  
Claudia B. Medeiros ◽  
Heloisa V. Rocha
Keyword(s):  
Problem Solving ◽  
Case Based Reasoning ◽  
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.

A Blackboard Architecture to Support Case-Based Reasoning in Mechanical Design

1992 ◽  
Author(s):  
Theodore Bardsz ◽  
Ibrahim Zeid
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Mechanical Design ◽  
Maximum Amount ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Design Plan ◽  
Case Based

Abstract One of the most significant issues in applying case-based reasoning (CBR) to mechanical design is to integrate previously unrelated design plans towards the solution of a new design problem. The total design solution (the design plan structure) can be composed of both retrieved and dynamically generated design plans. The retrieved design plans must be mapped to fit the new design context, and the entire design plan structure must be evaluated. An architecture utilizing opportunistic problem solving in a blackboard environment is used to map and evaluate the design plan structure effectively and successfuly. The architecture has several assets when integrated into a CBR environment. First, the maximum amount of information related to the design is generated before any of the mapping problems are addressed. Second, mapping is preformed as just another action toward the evaluation of the design plan. Lastly, the architecture supports the inclusion of memory elements from the knowledge base in the design plan structure. The architecture is implemented using the GBB system. The architecture is part of a newly developed CBR System called DEJAVU. The paper describes DEJAVU and the architecture. An example is also included to illustrate the use of DEJAVU to solve engineering design problems.

Case-Based Reasoning and Some Typical Applications

2014 ◽  
pp. 229-267 ◽  
Author(s):  
Durga Prasad Roy ◽  
Baisakhi Chakraborty
Keyword(s):  
Problem Solving ◽  
Machine Intelligence ◽  
Case Based Reasoning ◽  
Engineering System ◽  
Yale University ◽  
Control Pattern ◽  
Clustering And Classification ◽  
How People Learn ◽  
Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

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.

scholarly journals case-Based Reasoning (cBR) dan Pengaruhnya Terhadap Peningkatkan Kemampuan Pemecahan Masalah

2016 ◽  
Vol 4 (1) ◽  
pp. 33
Author(s):  
Deni Darmawan ◽  
Ayu Puji Rahayu
Keyword(s):  
High School ◽  
Problem Solving ◽  
Experimental Research ◽  
Case Based Reasoning ◽  
Z Score ◽  
Problem Solving Skills ◽  
Quasi Experimental ◽  
Pbl Model ◽  
Bahasa Indonesia ◽  
Case Based

AbstractThis study focuses on e orts to solve the problems of the low abil- ity students in solving di culties in learning Indonesian language as a subject ma er. The objective of this research is directed at ef- forts of explaining the in uence of Case-based Reasoning (CBR) in the learning towards the students’ problem-solving abilities. The method is a quasi-experimental research focusing on students of MA (Madrasah Aliyah or Islamic Senior High School) in one of the districts in West Java, where the learning in the control class using the Problem Based Learning (PBL). The research showed that the students’ problem-solving abilities by using CBR model is 44% in the high interpretation and by 56% in the moderate interpretation. Whereas the learning using the PBL model is 28% in the high inter- pretation, 56% in the medium interpretation, and 16% in the low interpretation. The value obtained through testing the hypothesis is z-score = -3089 smaller than z-table = -1.64. It means that Ho is refused and Ha is accepted. It further means that there is a sig- ni cant di erence between the problem-solving ability of students of using CBR model in learning and the students using PBL model in learning. The conclusion of this study indicates that the use of CBR model (designed for the study) has proved to give an e ect to the problem-solving skills of students learning Indonesian subject. AbstrakPenelitian ini memfokuskan pada upaya pemecahan per- masalahan mengenai rendahnya kemampuan siswa dalam memecahkan kesulitan-kesulitan ketika mempelajari materi pelajaran Bahasa Indonesia. Tujuan penelitian ini diarah- kan pada upaya menjelaskan pengaruh Case-Based Reasoning (CBR) dalam pembelajaran terhadap kemampuan pemeca- han masalah siswa. Metode penelitian adalah kuasi eksperi- men dengan objek penelitian adalah siswa MA di salah satu kabupaten di Jawa Barat, di mana pembelajaran yang diguna- kan oleh kelas kontrol adalah Pembelajaran Berbasis Masalah (PBL). Hasil penelitian menunjukkan bahwa kemampuan pe- mecahan masalah siswa dengan menggunakan model pembe- lajaran CBR sebesar 44% dalam interpretasi tinggi dan sebe- sar 56% dalam interpretasi sedang. Sedangkan pembelajaran menggunakan model pembelajaran PBL sebesar 28% dalam interpretasi tinggi, sebesar 56% dalam interpretasi sedang dan 16% dalam interpretasi rendah. Sementara berdasarkan hasil uji hipotesis diperoleh nilai zhitung = -3.089 lebih kecil dari ztabel = -1.64. Hasil pengujian ini menunjukan bahwa Ho ditolak atau Ha diterima, artinya ada perbedaan secara signifikan antara kemampuan pemecahan masalah siswa yang menggunakan model pembelajaran CBR dan siswa yang menggunakan model pembelajaran PBL. Simpulan penelitian ini menunjukkan bahwa penggunaan CBR yang dirancang selama penelitian ternyata terbukti memberikan pengaruh terhadap kemampuan pemecahan masalah oleh siswa

scholarly journals CASE-BASED REASONING (CBR) DAN PENGARUHNYA TERHADAP PENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH

2016 ◽  
Vol 4 (1) ◽  
pp. 33
Author(s):  
Deni Darmawan ◽  
Ayu Puji Rahayu
Keyword(s):  
High School ◽  
Problem Solving ◽  
Case Based Reasoning ◽  
Z Score ◽  
Problem Solving Skills ◽  
Significant Difference ◽  
Quasi Experimental ◽  
Pbl Model ◽  
Bahasa Indonesia ◽  
Case Based

This study focuses on efforts to solve the problems of the low ability students in solving difficulties in learning Indonesian language as a subject matter. The objective of this research is directed at efforts of explaining the influence of Case based Reasoning (CBR) in the learning towards the students’ problem-solving abilities. The method is a quasi-experimental research focusing on students of MA (Madrasah Aliyah or Islamic Senior High School) in one of the districts in West Java, where the learning in the control class using the Problem Based Learning (PBL). The research showed that the students’ problem-solving abilities by using CBR model is 44% in the high interpretation and by 56% in the moderate interpretation. Whereas the learning using the PBL model is 28% in the high interpretation, 56% in the medium interpretation, and 16% in the low interpretation. The value obtained through testing the hypothesis is z-score = -3089 smaller than z-table = -1.64. It means that Ho is refused and Ha is accepted. It further means that there is a significant difference between the problem-solving ability of students of using CBR model in learning and the students using PBL model in learning. The conclusion of this study indicates that the use of CBR model (designed for the study) has proved to give an effect to the problem-solving skills of students learning Indonesian subject. AbstrakPenelitian ini memfokuskan pada upaya pemecahan permasalahan mengenai rendahnya kemampuan siswa dalam memecahkan kesulitan-kesulitan ketika mempelajari materi pelajaran Bahasa Indonesia. Tujuan penelitian ini diarahkan pada upaya menjelaskan pengaruh Case-Based Reasoning (CBR) dalam pembelajaran terhadap kemampuan pemecahan masalah siswa. Metode penelitian adalah kuasi eksperimen dengan objek penelitian adalah siswa MA di salah satu kabupaten di Jawa Barat, di mana pembelajaran yang digunakan oleh kelas kontrol adalah Pembelajaran Berbasis Masalah (PBL). Hasil penelitian menunjukkan bahwa kemampuan pemecahan masalah siswa dengan menggunakan model pembelajaran CBR sebesar 44% dalam interpretasi tinggi dan sebesar 56% dalam interpretasi sedang. Sedangkan pembelajaran menggunakan model pembelajaran PBL sebesar 28% dalam interpretasi tinggi, sebesar 56% dalam interpretasi sedang dan 16% dalam interpretasi rendah. Sementara berdasarkan hasil uji hipotesis diperoleh nilai zhitung = -3.089 lebih kecil dari ztabel = -1.64. Hasil pengujian ini menunjukan bahwa Ho ditolak atau Ha diterima, artinya ada perbedaan secara signifikan antara kemampuan pemecahan masalah siswa yang menggunakan model pembelajaran CBR dan siswa yang menggunakan model pembelajaran PBL. Simpulan penelitian ini menunjukkan bahwa penggunaan CBR yang dirancang selama penelitian ternyata terbukti memberikan pengaruh terhadap kemampuan pemecahan masalah oleh siswa 

scholarly journals The Case for Case-Based Transfer Learning

AI Magazine ◽  
2011 ◽  
Vol 32 (1) ◽  
pp. 54 ◽  
Author(s):  
Matthew Klenk ◽  
David W. Aha ◽  
Matt Molineaux
Keyword(s):  
Problem Solving ◽  
Transfer Learning ◽  
Future Research ◽  
Case Based Reasoning ◽  
Learning Method ◽  
Improve Performance ◽  
Learning Methods ◽  
Problem Solving Process ◽  
Case Based ◽  
Solution Transfer

Case-based reasoning (CBR) is a problem-solving process in which a new problem is solved by retrieving a similar situation and reusing its solution. Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and/or learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems. We describe examples of these approaches from our own and related work and discuss applicable transfer distances for each. We close with conclusions and directions for future research applying CBR to transfer learning.

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