A Review of the Development and Future Challenges of Case-Based Reasoning
Abstract
:1. Introduction
2. Basic Framework and Concept of CBR
- (1)
- Case retrieval: One or more source cases most similar to the new case are retrieved from the case base.
- (2)
- Case re-use: Information and knowledge from similar cases are re-used to establish solutions adapted to new case.
- (3)
- Case revision: The proposed solution is evaluated, and the solution is adjusted if it does not meet the requirements.
- (4)
- Case retention: The parts of this experience that may be useful for solving problems in the future are retained.
3. Development of CBR Key Technologies
3.1. Case Representation
3.2. Similarity Measure and Case Retrieval
3.2.1. Similarity Measure
- (1)
- An improved method based on a hybrid similarity measure [32,35,36,37] mainly improves the calculation accuracy of similarity by processing attribute features, such as adding other information and setting multiple attribute value formats. This hybrid measurement method solves the similarity measurement problem of multi-attribute representation cases, but it often needs to combine the relevant knowledge of specific applications, which is a highly professional task and computationally complex.
- (2)
- Based on the weighted similarity measure of feature weight optimization [38,39,40,41,42], the measurement calculation is improved through the reasonable distribution of feature weight (i.e., in Formula (3)). The emphasis is on the selection, optimization, and improvement of the weight distribution method. This kind of method has been studied for the longest amount of time, and the achievements are more fruitful. It combines information entropy, genetic algorithm, neural network, and other optimization algorithms. However, due to the different evaluation criteria in each article, how to choose the appropriate optimization algorithm in practical applications is a difficult problem.
- (3)
- Based on the (deep) metric learning algorithm [33,43,44], the learning of the similarity measure is achieved by training a (deep) neural network. This method has a shorter research time in CBR compared with that of other case similarity measurement algorithms. Its advantage is that it realizes similarity calculations in the form of a neural network, solves the nonlinear problem, and reduces the computational complexity of the similarity measurement process. In dealing with cases represented by text and images, etc., the network structure has better representation advantages in case data processing; however, problems such as the neural network design caused by this method also need to be considered and studied.
3.2.2. Case Retrieval
- The representation of the objects.
- The case base structure.
- The similarity measure.
- The accuracy of the intended answer or solution.
- (1)
- (2)
- (3)
3.3. Case Adaptation
- Substitutions: They replace some part of the retrieved solution by another or by several others.
- Structural transformations: They alter the structure of the solution and re-organize the solution by adding, deleting, or replacing parts of the proposed solution [70].
- Generative adaptations: They replay the method of deriving the retrieved solution on the new problem. This is the most complex form of adaptation.
- (1)
- Improvement of the CDH method [61,77,78,79]. Although the CDH adaptation method reduces the burden of knowledge engineering, it also brings the problem of defining the difference function. Recent studies have used implicit calculation in ML technology to replace the traditional CDH difference calculation, but there is still a lack of theoretical proof.
- (2)
- Adaptation method based on knowledge and rules [80,81,82]. Lieber et al. [81] proposed using positive examples to adapt rules, while using negative examples to filter out some of the rules to avoid the problem of incorrect schemes in CBR systems during the adaptation process. These methods focus on the extraction efficiency of knowledge and rules to enhance adaptation performance.
- (3)
- Adaptation methods based on machine learning (ML) or DL [51,83,84]. Long et al. [84] proposed a feature re-use case adaptation (FR-CA) method based on an SVR machine, which can automatically and intelligently solve product experience features and achieve the integration of expert comprehensive decisions with the least expert participation. These methods simplify the compilation and extraction of adapted knowledge through an end-to-end learning process, but the design of network structures often affects the learning effect.
3.4. Case Base Maintenance (CBM)
- (1)
- (2)
- (3)
4. Application Fields of CBR
4.1. Diagnosis
4.2. Prediction
4.3. Design and Planning
4.4. Decision Support
4.5. Recommendation System
4.6. Other Applications
5. Summary and Challenge
Author Contributions
Funding
Conflicts of Interest
References
- Aha, D.W. The omnipresence of case-based reasoning in science and application. Knowl.-Based Syst. 1998, 11, 261–273. [Google Scholar] [CrossRef]
- Aleven, V. Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment. Artif. Intell. 2003, 150, 183–237. [Google Scholar] [CrossRef]
- Ni, P.; Liu, B.; He, G. An online optimization strategy for a fluid catalytic cracking process using a case-based reasoning. RSC Adv. 2021, 11, 28557–28564. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.Y.; Zhang, L.B.; Hu, J.Q.; Liu, Z.Y.; Xu, K.K. Emergency response recommendation for long-distance oil and gas pipeline based on an accident case representation model. J. Loss Prev. Process Ind. 2020, 77, 104779. [Google Scholar] [CrossRef]
- Chen, J.H.; Jia, X.L. A multimedia case-based reasoning framework for assembly sequence planning. Assem. Autom. 2019, 39, 673–684. [Google Scholar] [CrossRef]
- Duan, J.L.; Lin, Z.B.; Jiao, F.; Jiang, Y.X.; Chen, K.X. A dynamic case-based reasoning system for responding to infectious disease outbreaks. Expert Syst. Appl. 2022, 204, 117628. [Google Scholar] [CrossRef]
- Schank, R. Dynamic Memory: A Theory of Reminding and Learning in Computers and People; Cambridge University Press: Cambridge, UK, 1982. [Google Scholar]
- Riesbeck, C.K.; Schank, R. Inside Case-Based Reasoning; Lawrence Erlbaum Associates Inc.: Hillsdale, NJ, USA, 1989. [Google Scholar]
- Aamodt, A.; Plaza, E. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun. 1994, 7, 39–59. [Google Scholar] [CrossRef]
- Watson, I. Case-based reasoning is a methodology not a technology. Knowl.-Based Syst. 1999, 12, 303–308. [Google Scholar] [CrossRef]
- Finnie, G.; Sun, Z.H. R5 model for case-based reasoning. Knowl.-Based Syst. 2003, 16, 59–65. [Google Scholar] [CrossRef]
- Chuang, C.L. Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Inf. Sci. 2013, 236, 174–185. [Google Scholar] [CrossRef]
- Bergmann, R.; Kolodner, J.; Plaza, E. Representation in case-based reasoning. Knowl. Eng. Rev. 2005, 20, 209–213. [Google Scholar] [CrossRef]
- Amailef, K.; Lu, J. Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services. Decis. Support Syst. 2013, 55, 79–97. [Google Scholar] [CrossRef]
- De Mantaras, R.L.; Mcsherry, D.; Bridge, D.; Leake, D.; Smyth, B.; Craw, S.; Faltings, B.; Maher, M.L.; Cox, M.T.; Forbus, K.; et al. Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 2005, 20, 215–240. [Google Scholar] [CrossRef]
- Porter, B.W.; Bareiss, R.; Holte, R.C. Concept learning and heuristic classification in weak-theory domains. Artif. Intell. 1990, 45, 229–264. [Google Scholar] [CrossRef]
- Minsky, M. A Framework for Representing Knowledge. In MIT-AI Laboratory Memo; MIT AI Lab: Cambridge, MA, USA, 1974. [Google Scholar]
- Qian, Q.; Zhang, R.; Che, H.Y. Object-oriented case representation and its application in IDS. In Proceedings of the 8th IEEE/ACIS International Conference on Computer and Information Science, Shanghai, China, 1–3 June 2009; pp. 301–306. [Google Scholar]
- Neves, J.; Goncalves, N.; Oliveira, R.; Gomes, S.; Neves, J.; Macedo, J.; Abelha, A.; Analide, C.; Machado, J.; Santos, M.F.; et al. Screening a case base for stroke disease detection. In Proceedings of the 11th International Conference on Hybrid Artificial Intelligence Systems (HAIS), Seville, Spain, 18–20 April 2016; Volume 9648, pp. 18–20. [Google Scholar]
- Zeilinger, H.; Perner, A.; Kohlhauser, S. Bionically inspired information representation module. In Proceedings of the 3rd Annual International Conference on Human System Interaction (HSI), Rzeszow, Poland, 13–15 May 2010; Volume 9648, pp. 13–15. [Google Scholar]
- Kovacs, S. Adapting the scale and move FRI for the fuzziness interpolation of the double fuzzy point rule representation. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ), Hyderabad, India, 7–10 July 2013; pp. 7–10. [Google Scholar]
- Li, C.D.; Wang, D.; Yang, W.M. Case representation and retrieval for complex product design based on case-based reasoning. J. Intell. Fuzzy Syst. 2022, 43, 2985–3002. [Google Scholar] [CrossRef]
- Sharma, A.; Sharma, A.; Deodhare, D.; Chakraborti, S.; Kumar, P.S.; Mitra, P.P. Case representation and retrieval techniques for neuroanatomical connectivity extraction from PubMed. In Proceedings of the 24th International Conference on Case-Based Reasoning (ICCBR), Atlanta, GA, USA, 31 October–2 November 2016; Volume 9969, pp. 370–386. [Google Scholar]
- Guo, Y.; Zhang, B.; Sun, Y.; Jiang, K.; Wu, K. Machine learning based feature selection and knowledge reasoning for CBR system under big data. Pattern Recognit. 2020, 112, 107805. [Google Scholar] [CrossRef]
- Zhu, L.N.; Ren, K.; Pu, J.Y. Framework case decision reasoning method integrating multiple information. In Proceedings of the 6th International Conference on Electromechanical Control Technology and Transportation (ICECTT), Chongqing, China, 14–16 May 2021; Volume 12081, p. 1208143. [Google Scholar]
- Dong, J.W.; Jing, X.W.; Lu, X.; Liu, J.F.; Cao, X.W.; Du, C.; Li, J.; Li, L. Process knowledge graph modeling techniques and application methods for ship heterogeneous models. Sci. Rep. 2022, 12, 2911. [Google Scholar] [CrossRef] [PubMed]
- Bannour, W.; Maalel, A.; Ben Ghezala, H.H. Case-based reasoning for crisis response: Case representation and case retrieval. Procedia Comput. Sci. 2020, 176, 1063–1072. [Google Scholar] [CrossRef]
- López-Sánchez, D.; Herrero, J.R.; Arrieta, A.G.; Corchado, J.M. Hybridizing metric learning and case-based reasoning for adaptable clickbait detection. Appl. Intell. 2017, 48, 2976–2982. [Google Scholar] [CrossRef]
- Amin, K.; Stelios, K.; Althoff, K.D.; Dengel, A.; Petridis, M. Cases without borders: Automating knowledge acquisition approach using deep autoencoders and Siamese networks in case-based reasoning. In Proceedings of the 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 4–6 November 2019; pp. 133–140. [Google Scholar]
- Leake, D.; Crandall, D. On bringing case-based reasoning methodology to deep learning. In Proceedings of the 28th Case-Based Reasoning Research and Development (ICCBR), Salamanca, Spain, 8–12 June 2020; Volume 12311, pp. 343–348. [Google Scholar]
- Bergmann, R.; Stahl, A. Similarity measures for object-oriented case representations. In Proceedings of the 4th European Workshop on Case-Based Reasoning (EWCBR), Dublin, Ireland, 23–25 September 1998; Volume 1488, pp. 25–36. [Google Scholar]
- Zheng, J.; Wang, Y.M.; Lin, Y. Hybrid multi-attribute case retrieval method based on intuitionistic fuzzy and evidence reasoning. J. Intell. Fuzzy Syst. 2019, 36, 1–12. [Google Scholar] [CrossRef]
- Mathisen, B.M.; Aamodt, A.; Bach, K.; Langseth, H. Learning similarity measures from data. Prog. Artif. Intell. 2019, 9, 129–143. [Google Scholar] [CrossRef]
- Yan, A.J.; Yu, H.; Wang, D.H. Case-based reasoning classifier based on learning pseudo metric retrieval. Expert Syst. Appl. 2017, 89, 91–98. [Google Scholar] [CrossRef]
- Wang, H.Q.; Sun, B.B.; Shen, X.F. Hybrid similarity measure for retrieval in case-based reasoning systems and its applications for computer numerical control turret design. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2018, 232, 918–927. [Google Scholar] [CrossRef]
- Lin, K.S. A Case-based reasoning system for interior design using a new cosine similarity retrieval algorithm. J. Inf. Telecommun. 2020, 4, 91–104. [Google Scholar] [CrossRef]
- Dolphin, R.; Smyth, B.; Xu, Y.; Dong, R.H. Measuring financial time series similarity with a view to identifying profitable stock market opportunities. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 64–78. [Google Scholar]
- Kar, D.; Chakraborti, S.; Ravindran, B. Feature weighting and confidence based prediction for case based reasoning systems. In Proceedings of the 20th International Conference on Case-Based Reasoning (ICCBR), Lyon, France, 3–6 September 2012; Volume 7466, pp. 211–225. [Google Scholar]
- Gu, D.X.; Liang, C.H.; Zhao, H.M. A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis. Artif. Intell. Med. 2017, 77, 31–47. [Google Scholar] [CrossRef] [PubMed]
- Kwon, N.; Song, K.; Park, M.; Jang, Y.J.; Yoon, I.; Ahn, Y. Preliminary service life estimation model for MEP components using case-based reasoning and genetic algorithm. Sustainability 2019, 11, 3074. [Google Scholar] [CrossRef]
- Yan, X.; Tu, N.W.; Wu, S.W.; Zhu, Y.H. Dynamic prediction of coal and gas outburst based on BPNN and case-based reasoning. In Proceedings of the 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China, 23–27 July 2019; pp. 1–6. [Google Scholar]
- Ha, J.B.; Shi, S.Y.; Wang, Y.W. Application of case retrieval algorithm based on variable weighted grey relation in urban rail transit network operation pattern database. In Proceedings of the 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), Chongqing, China, 28–30 November 2020; pp. 274–277. [Google Scholar]
- Hoffmann, M.; Malburg, L.; Klein, P.; Bergmann, R. Using siamese graph neural networks for similarity-based retrieval in process-oriented case-based reasoning. In Proceedings of the 28th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 8–12 June 2020; Volume 12311, pp. 229–244. [Google Scholar]
- Li, J.G.; Xu, C.J.; Zhang, T. Similarity measure of time series based on Siamese and sequential neural networks. In Proceedings of the 39th Chinese Control Conference (CCC), Shenyang, China, 27–29 July 2020; Volume 2020, pp. 6408–6413. [Google Scholar]
- Verma, D.; Bach, K.; Mork, P.J. Similarity measure development for case-based reasoning-a data-driven approach. In Proceedings of the 3rd Nordic Artificial Intelligence Research and Development, Trondheim, Norway, 27–28 May 2019; Volume 1056, pp. 143–148. [Google Scholar]
- Lenz, M.; Ollinger, S.; Sahitaj, P.; Bergmann, R. Semantic textual similarity measures for case-based retrieval of argument graphs. In Proceedings of the 27th International Conference on Case-Based Reasoning (ICCBR), Trondheim, Norway, 27–28 May 2019; Voume 11680, pp. 219–234. [Google Scholar]
- Zeyen, C.; Bergmann, R. A*-based similarity assessment of semantic graphs. In Proceedings of the 28th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 8–12 June 2020; Volume 12311, pp. 17–32. [Google Scholar]
- Wu, Y.J.; Zhou, J.T. A contextual information-augmented probabilistic case-based reasoning model for knowledge graph reasoning. In Proceedings of the 31st Case-Based Reasoning Research and Development (ICCBR), Aberdeen, UK, 17–20 July 2023; Volume 14141, pp. 102–117. [Google Scholar]
- Kang, J.M.; Mokbel, M.F.; Shekhar, S.; Xia, T.; Zhang, D.H. Incremental and general evaluation of reverse nearest neighbors. IEEE Trans. Knowl. Data Eng. 2010, 22, 983–999. [Google Scholar] [CrossRef]
- Rallabandi, V.P.S.; Sett, S.K. Knowledge-based image retrieval system. Knowl.-Based Syst. 2008, 21, 89–100. [Google Scholar] [CrossRef]
- Sharifi, M.; Naghibzadeh, M.; Rouhani, M. Adaptive case-based reasoning using support vector regression. In Proceedings of the 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, 22–23 February 2013; pp. 1006–1010. [Google Scholar]
- Wang, S.; Wang, J.K.; Han, Y.H. Pattern matching strategy based on optimized fuzzy clustering algorithm for industrial internet. In Proceedings of the 40th Chinese Control Conference (CCC), Guangzhou, China, 27–30 July 2019; Volume 2019, pp. 7005–7009. [Google Scholar]
- Zhai, Z.H.; Ortega, J.F.M.; Martinez, N.L.; Xu, H.L. An efficient case retrieval algorithm for agricultural case-based reasoning systems, with consideration of case base maintenance. Agriculture 2020, 10, 387. [Google Scholar] [CrossRef]
- Cui, X.L.; Cai, S.Q.; Qin, Y.C. Similarity-based approach for accurately retrieving similar cases to intelligently handle online complaints. Kybernetes 2017, 46, 1223–1244. [Google Scholar] [CrossRef]
- Wang, H.; Meng, X.H.; Zhu, X.Y. Improving knowledge capture and retrieval in the BIM environment: Combining case-based reasoning and natural language processing. Autom. Constr. 2022, 139, 104317. [Google Scholar] [CrossRef]
- Mulayim, M.O.; Arcos, J.L. Fast anytime retrieval with confidence in large-scale temporal case bases. Knowl.-Based Syst. 2020, 206, 106374. [Google Scholar] [CrossRef]
- Portinale, L. Integrating knn retrieval with inference on graphical models in case-based reasoning. In Proceedings of the 32nd Case-Based Reasoning Research and Development (ICCBR), Merida, Mexico, 1–4 July 2024; Volume 14775, pp. 1–16. [Google Scholar]
- Abbas, F.; Najjar, N.; Wilson, D. The bites eclectic: Critique-based conversational recommendation for diversity-focused meal planning. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 1–16. [Google Scholar]
- McGinty, L.; Smyth, B. On the role of diversity in conversational recommender systems. In Proceedings of the 5th International Conference on Case-Based Reasoning (ICCBR), Trondheim, Norway, 23–26 June 2003; Volume 2689, pp. 276–290. [Google Scholar]
- Smyth, B.; Keane, M.T. Experiments on adaptation-guided retrieval in case-based design. In Proceedings of the 1st International Conference on Case-Based Reasoning (ICCBR), Sesimbra, Portugal, 23–26 October 1995; Volume 1010, pp. 313–324. [Google Scholar]
- David, L.; Ye, X.M. Harmonizing case retrieval and adaptation with alternating optimization. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 125–139. [Google Scholar]
- Jalali, V.; Leake, D. An ensemble approach to adaptation-guided retrieval. In Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference (FLAIRS), Pensacola Beach, FL, USA, 21–23 May 2014; pp. 295–300. [Google Scholar]
- McSherry, D. Explanation in recommender systems. Artif. Intell. Rev. 2005, 24, 179–197. [Google Scholar] [CrossRef]
- Doyle, D.; Cunningham, P.; Bridge, D.; Rahman, Y. Explanation oriented retrieval. Lect. Notes Comput. Sci. 2004, 3155, 157–168. [Google Scholar]
- Darias, J.M.; Caro-Martínez, M.; Díaz-Agudo, B.; Recio-Garcia, J.A. Using case-based reasoning for capturing expert knowledge on explanation methods. In Proceedings of the 30th Case-Based Reasoning Research and Development (ICCBR), Nancy, France, 12–15 September 2022; Volume 13405, pp. 3–17. [Google Scholar]
- Chang, J.W.; Lee, M.C.; Wang, T.I. Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore. Comput. Ind. J. Elsevier 2016, 78, 29–42. [Google Scholar] [CrossRef]
- Lepage, Y.; Lieber, J.; Mornard, I.; Nauer, E.; Romary, J.; Sies, R. The French correction: When retrieval is harder to specify than adaptation. In Proceedings of the 28th Case-Based Reasoning Research and Development (ICCBR), Salamanca, Spain, 8–12 June 2020; Volume 12311, pp. 309–324. [Google Scholar]
- Wilkerson, Z.; Leake, D.; Crandall, D.J. On combining knowledge-engineered and network-extracted features for retrieval. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 248–262. [Google Scholar]
- Low, N.; Hesser, J.; Blessing, M. Multiple retrieval case-based reasoning for incomplete datasets. J. Biomed. Inform. 2019, 92, 103127. [Google Scholar] [CrossRef] [PubMed]
- Kolodner, J. Case-Based Reasoning (Morgan Kaufmann Series in Representation and Reasoning); Morgan Kaufmann: San Francisco, CA, USA, 1993. [Google Scholar]
- Craw, S. Introspective learning to build case-based reasoning (CBR) knowledge containers. In Proceedings of the 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM), Leipzig, Germany, 5–7 July 2003; Volume 2734, pp. 1–6. [Google Scholar]
- Shiu, S.C.K.; Yeung, D.S.; Sun, C.H.; Wang, X.Z. Transferring case knowledge to adaptation knowledge: An approach for case-base maintenance. Comput. Intell. 2001, 17, 295–314. [Google Scholar] [CrossRef]
- Qi, J.; Hu, J.; Peng, Y.H. A modularized case adaptation method of case-based reasoning in parametric machinery design. Eng. Appl. Artif. Intell. 2017, 64, 352–366. [Google Scholar] [CrossRef]
- Minor, M.; Bergmann, R.; Gorg, S. Case-based adaptation of workflows. Inf. Syst. 2014, 40, 142–152. [Google Scholar] [CrossRef]
- Leake, D.; Powell, J. Mining large-scale knowledge sources for case adaptation knowledge. In Proceedings of the 7th International Conference on Case-Based Reasoning (ICCBR), Northern Ireland, UK, 13–16 August 2007; Volume 4626, pp. 209–223. [Google Scholar]
- Hanney, K.; Keane, M.T. Learning adaptation rules from a case-base. In Proceedings of the 3rd European Workshop on Advances in Case-Based Reasoning (EWCBR), Lausanne, Switzerland, 14–16 November 1996; Volume 1168, pp. 179–192. [Google Scholar]
- Jalali, V.; Leake, D.; Forouzandehmehr, N. Ensemble of adaptations for classification: Learning adaptation rules for categorical features. In Proceedings of the 24th International Conference on Case-Based Reasoning (ICCBR), Atlanta, GA, USA, 31 October–2 November 2016; Volume 9969, pp. 186–202. [Google Scholar]
- Liao, C.K.; Liu, A.; Chao, Y.S. A machine learning approach to case adaptation. In Proceedings of the First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Laguna Hills, CA, USA, 26–28 September 2018; pp. 106–109. [Google Scholar]
- Yan, A.J.; Zhang, K.H.; Yu, Y.H.; Wang, P. An attribute difference revision method in case-based reasoning and its application. Eng. Appl. Artif. Intell. 2017, 65, 212–219. [Google Scholar] [CrossRef]
- Jalali, V.; Leake, D. Enhancing case-based regression with automatically-generated ensembles of adaptations. J. Intell. Inf. Syst. 2016, 46, 237–258. [Google Scholar] [CrossRef]
- Lieber, J.; Nauer, E. Adaptation knowledge discovery using positive and negative cases. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 140–155. [Google Scholar]
- Malburg, L.; Hotz, M.; Bergmann, R. Improving complex adaptations in process-oriented case-based reasoning by applying rule-based adaptation. In Proceedings of the 32nd Case-Based Reasoning Research and Development (ICCBR), Merida, Mexico, 1–4 July 2024; Volume 14775, pp. 50–66. [Google Scholar]
- Glatt, R.; Da Silva, F.L.; da Costa Bianchi, R.A.; Costa, A.H.R. DECAF: Deep case-based policy inference for knowledge transfer in reinforcement learning. Expert Syst. Appl. 2020, 156, 113420. [Google Scholar] [CrossRef]
- Long, X.J.; Li, H.T.; Du, Y.F.; Mao, E.R.; Tai, J.J. A knowledge-based automated design system for mechanical products based on a general knowledge framework. Expert Syst. Appl. 2021, 178, 114960. [Google Scholar] [CrossRef]
- Leake, D.B.; Wilson, D.C. Categorizing case-base maintenance: Dimensions and directions. In Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning (EWCBR), Dublin, Ireland, 23–25 September 1998; Volume 1488, pp. 196–207. [Google Scholar]
- Lupiani, E.; Juarez, J.M.; Palma, J. Evaluating case-base maintenance algorithms. Knowl.-Based Syst. 2014, 67, 180–194. [Google Scholar] [CrossRef]
- Smiti, A.; Elouedi, Z. WCOID-DG: An approach for case base maintenance based on Weighting, Clustering, Outliers, Internal Detection and Dbsan-Gmeans. J. Comput. Syst. Sci. 2014, 80, 27–38. [Google Scholar] [CrossRef]
- Nakhjiri, N.; Salamo, M.; Sanchez-marre, M. Reputation-based maintenance in case-based reasoning. Knowl.-Based Syst. 2020, 193, 105283. [Google Scholar] [CrossRef]
- Smyth, B.; McKenna, E. Building compact competent case-bases. In Proceedings of the 3rd International Conference on Case-Based Reasoning (ICCBR), Seeon Monastery, Germany, 27–30 July 1999; Volume 1650, pp. 329–342. [Google Scholar]
- Zhu, J.; Yang, Q. Remembering to add: Competence-preserving case-addition policies for case-base maintenance. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 31 July–6 August 1999; Volume 99, pp. 234–241. [Google Scholar]
- Leake, D.B.; Wilson, D.C. Remembering why to remember: Performance-guided case-base maintenance. In Proceedings of the 5rd European Workshop on Advances in Case-Based Reasoning (EWCBR), Trento, Italy, 6–9 September 2000; Volume 1898, pp. 161–172. [Google Scholar]
- Segata, N.; Blanzieri, E.; Cunningham, P. A scalable noise reduction technique for large case-based systems. In Proceedings of the 8th International Conference on Case-Based Reasoning (ICCBR), Seattle, WA, USA, 20–23 July 2009; Volume 5650, pp. 328–342. [Google Scholar]
- Chebel-Morello, B.; Haouchine, M.K.; Zerhouni, N. Case-based maintenance: Structuring and incrementing the case base. Knowl.-Based Syst. 2015, 88, 165–183. [Google Scholar] [CrossRef]
- Lu, N.; Zhang, G.Q.; Lu, J. Concept drift detection via competence models. Artif. Intell. 2014, 209, 11–28. [Google Scholar] [CrossRef]
- Chebli, A.; Djebbar, A.; Merouani, H.F.; Lounis, H. Case-base maintenance: An approach based on active semi-supervised learning. Int. J. Pattern Recognit. Artif. Intell. 2021, 35, 2151011. [Google Scholar] [CrossRef]
- Smiti, A.; Elouedi, Z. Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems. Theor. Comput. Sci. 2020, 817, 24–32. [Google Scholar] [CrossRef]
- Yang, S.K.; Bian, C.; Li, X.; Tan, L.; Tang, D.X. Optimized fault diagnosis based on FMEA-style CBR and BN for embedded software system. Int. J. Adv. Manuf. Technol. 2018, 94, 3441–3453. [Google Scholar] [CrossRef]
- Chen, M.Q.; Qu, R.; Fang, W.G. Case-based reasoning system for fault diagnosis of aero-engines. Expert Syst. Appl. 2022, 202, 117350. [Google Scholar] [CrossRef]
- Benamina, M.; Atmani, B.; Benbelkacem, S. Diabetes diagnosis by case-based reasoning and fuzzy logic. Int. J. Interact. Multimed. Artif. Intell. 2018, 5, 72–80. [Google Scholar] [CrossRef]
- Zhang, L.; Qi, P.; Zhang, H. Research on auxiliary diagnosis technology of chronic disease based on case-based reasoning. In Proceedings of the 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, 27–28 August 2021; pp. 322–328. [Google Scholar]
- Feng, K.; Xu, A.J.; He, D.F.; Yang, L.Z. Case-based reasoning method based on mechanistic model correction for predicting endpoint sulphur content of molten iron in KR desulphurization. Ironmak. Steelmak. 2020, 47, 799–806. [Google Scholar] [CrossRef]
- Yan, A.J.; Shao, H.S.; Wang, P. A soft-sensing method of dissolved oxygen concentration by groupgenetic case-based reasoning with integrating group decision making. Neurocomputing 2015, 169, 422–429. [Google Scholar] [CrossRef]
- Bebarta, D.K.; Das, T.K.; Chowdhary, C.L.; Gao, X.Z. An intelligent hybrid system for forecasting stock and forex trading signals using optimized recurrent FLANN and case-based reasoning. Int. J. Comput. Intell. Syst. 2021, 14, 1763–1772. [Google Scholar] [CrossRef]
- Shao, J.F.; Liang, C.Y.; Liu, Y.J.; Xu, J.; Zhao, S.P. Relief demand forecasting based on intuitionistic fuzzy case-based reasoning. Socio-Econ. Plan. Sci. 2021, 74, 100932. [Google Scholar] [CrossRef]
- Feely, C.; Caulfield, B.; Lawlor, A.; Smyth, B. A case-based reasoning approach to predicting and explaining running related injuries. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 79–93. [Google Scholar]
- Bai, Y.B.; Chen, D.; Zhang, Z.Y. A conditional generative adversarial networks-augmented case-based reasoning framework for crop yield predictions with time-series remote sensing data. In Proceedings of the 32nd International Conference on Case-Based Reasoning (ICCBR), Merida, Mexico, 1 July 2024; Volume 3708, pp. 192–205. [Google Scholar]
- Grace, K.; Maher, M.L.; Wilson, D.C.; Najjar, N.A. Combining CBR and deep learning to generate surprising recipe designs. In Proceedings of the 24th Case-Based Reasoning Research and Development (ICCBR), Atlanta, GA, USA, 31 October–2 November 2016; Volume 9969, pp. 154–169. [Google Scholar]
- Huo, Y.L.; Liu, J.B.; Xiong, J.; Xiao, W.J.; Zhao, J.F. Machine learning and CBR integrated mechanical product design approach. Adv. Eng. Inform. 2022, 52, 101611. [Google Scholar] [CrossRef]
- Ke, C.; Jiang, Z.G.; Zhang, H. An intelligent design for remanufacturing method based on vector space model and case-based reasoning. J. Clean. Prod. 2022, 277, 123269. [Google Scholar] [CrossRef]
- Jiang, Z.G.; Jiang, Y.; Wang, Y.; Zhang, H.; Cao, H.J.; Tian, G.D. A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J. Intell. Manuf. 2019, 30, 19–32. [Google Scholar] [CrossRef]
- Xie, R.; Luo, M.Q.; Chen, Z.K.; Liu, W.H. A multi-CBR algorithm based on comprehensive evaluation for operation planning of helicopter. IEEE Access 2020, 8, 124110. [Google Scholar] [CrossRef]
- Abdelwahed, M.F.; Mohamed, A.E.; Saleh, M.A. Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms. Ain Shams Eng. J. 2020, 11, 133–142. [Google Scholar] [CrossRef]
- Schoenborn, J.M.; Althoff, K.D. A multi-agent case-based reasoning intrusion detection system prototype. In Proceedings of the 31st Case-Based Reasoning Research and Development (ICCBR), Aberdeen, UK, 17–20 July 2023; Volume 14141, pp. 359–374. [Google Scholar]
- Wang, D.J.; Liu, J.W.; Lin, Q.L.; Yu, H.L. A decision-making system based on case-based reasoning for predicting stroke rehabilitation demands in heterogeneous information environment. Appl. Soft Comput. 2024, 154, 111358. [Google Scholar] [CrossRef]
- Hamrouni, B.; Bourouis, A.; Korichi, A.; Brahmi, M. Explainable ontology-based intelligent decision support system for business model design and sustainability. Sustainability 2021, 13, 9819. [Google Scholar] [CrossRef]
- Khanmohammadi, E.; Safari, H.; Zandieh, M.; Malmir, B.; Tirkolaee, E.B. Development of dynamic balanced scorecard using case-based reasoning method and adaptive neuro-fuzzy inference system. IEEE Trans. Eng. Manag. 2024, 71, 899–912. [Google Scholar] [CrossRef]
- Ali, S.I.; Jung, S.W.; Bilal, H.S.M.; Lee, S.-H.; Hussain, J.; Afzal, M.; Hussain, M.; Ali, T.; Chung, T.; Lee, S. Clinical decision support system based on hybrid knowledge modeling: A case study of chronic kidney disease-mineral and bone disorder treatment. Int. J. Environ. Res. Public Health 2022, 19, 226. [Google Scholar] [CrossRef] [PubMed]
- Amin, K.; Kapetanakis, S.; Polatidis, N.; Althoff, K.D.; Dengel, A. DeepKAF: A heterogeneous CBR and deep learning approach for NLP prototyping. In Proceedings of the 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Novi Sad, Serbia, 24–26 August 2020; pp. 1–7. [Google Scholar]
- Bridge, D.; Goeker, M.H.; Mcginty, L.; Smyth, B. Case-based recommender systems. Knowl. Eng. Rev. 2005, 20, 315–320. [Google Scholar] [CrossRef]
- Soto-Forero, D.; Ackermann, S.; Betbeder, M.L.; Henriet, J. The intelligent tutoring system AI-VT with case-based reasoning and real time recommender models. In Proceedings of the 32nd Case-Based Reasoning Research and Development (ICCBR), Merida, Mexico, 1–4 July 2024; Volume 14775, pp. 191–205. [Google Scholar]
- Dong, R.H.; Smyth, B. Personalized opinion-based recommendation. In Proceedings of the 24th International Conference on Case-Based Reasoning (ICCBR), Atlanta, GA, USA, 31 October–2 November 2016; Volume 9969, pp. 93–107. [Google Scholar]
- Li, J.Y.; Wu, H.S.; Zuo, W.Y.; Tang, H.Y. Case-based reasoning for personalized recommender on user preference through dynamic clustering. In Proceedings of the 2nd International Conference on Computing and Data Science (CDS), Stanford, CA, USA, 28–30 January 2021; Volume 158, pp. 1–5. [Google Scholar]
- Raza, B.; Aslam, A.; Sher, A.; Malik, A.K.; Faheem, M. Autonomic performance prediction framework for data warehouse queries using lazy learning approach. Appl. Soft Comput. 2020, 91, 106216. [Google Scholar] [CrossRef]
- Yu, W.; Guo, X.T.; Chen, F.; Chang, T.; Wang, M.Z.; Wang, X.D. Similar questions correspond to similar SQL queries: A case-based reasoning approach for Text-to-SQL translation. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 294–308. [Google Scholar]
- Upadhyay, A.; Massie, S.; Singh, R.K.; Gupta, G.; Ojha, M. A case-based approach to Data-to-Text generation. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 232–247. [Google Scholar]
- Upadhyay, A.; Massie, S. CBR assisted context-aware surface realisation for data-to-text generation. In Proceedings of the 31st Case-Based Reasoning Research and Development (ICCBR), Aberdeen, UK, 17–20 July 2023; Volume 14141, pp. 34–49. [Google Scholar]
- Turner, J.T.; Floyd, M.W.; Gupta, K.; Oates, T. NOD-CC: A hybrid CBR-CNN architecture for novel object discovery. In Proceedings of the 27th International Conference on Case-Based Reasoning (ICCBR), Otzenhausen, Germany, 8–12 September 2019; Volume 11680, pp. 373–387. [Google Scholar]
- Corbat, L.; Nauval, M.; Henriet, J.; Lapayre, J.C. A fusion method based on deep learning and case-based reasoning which improves the resulting medical image segmentations. Expert Syst. Appl. 2020, 147, 113200. [Google Scholar] [CrossRef]
- Astier, E.; Iopeti, H.; Lieber, J.; Mathieu Steinbach, H.; Yvoz, L. Case-based cleaning of text images. In Proceedings of the 31st Case-Based Reasoning Research and Development (ICCBR), Aberdeen, UK, 17–20 July 2023; Volume 14141, pp. 344–358. [Google Scholar]
- Amin, K.; Kapetanakis, S.; Althoff, K.D.; Dengel, A.; Petridis, M. Answering with cases: A CBR approach to deep learning. In Proceedings of the 26th International Conference on Case-Based Reasoning (ICCBR), Stockholm, Sweden, 9–12 July 2018; Volume 11156, pp. 15–27. [Google Scholar]
- Confalonieri, R.; Coba, L.; Wagner, B.; Besold, T.R. A historical perspective of explainable Artificial Intelligence. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 11, e1391. [Google Scholar] [CrossRef]
- Recio-García, J.A.; Parejas-Llanovarced, H.; Orozco-del-Castillo, M.G.; Brito-Borges, E.E. A case-based approach for the selection of explanation algorithms in image classification. In Proceedings of the 29th International Conference on Case-Based Reasoning (ICCBR), Salamanca, Spain, 13–16 September 2021; Volume 12877, pp. 186–200. [Google Scholar]
- Weber, R.O.; Johs, A.J.; Li, J.; Huang, K. Investigating textual case-based XAI. In Proceedings of the 26th International Conference on Case-Based Reasoning (ICCBR), Stockholm, Sweden, 9–12 July 2018; Volume 11156, pp. 431–447. [Google Scholar]
- Nadeem, R.; Wu, H.; Paik, H.; Wang, C. A case based deep neural network interpretability framework and its user study. In Proceedings of the Web Information Systems Engineering—WISE 2019, Hong Kong, China, 19–22 January 2020; Volume 11881, pp. 147–161. [Google Scholar]
- Parejas-Llanovarced, H.; Darias, J.M.; Caro-Martínez, M.; Recio-Garcia, J.A. Selecting explanation methods for intelligent iot systems: A case-based reasoning approach. In Proceedings of the 31st International Conference on Case-Based Reasoning (ICCBR), Aberdeen, UK, 17–20 July 2023; Volume 14141, pp. 185–199. [Google Scholar]
- Caro-Martínez, M.; Darias, J.M.; Díaz-Agudo, B.; Recio-García, J.A. Use case-specific reuse of XAI strategies: Design and analysis through an evaluation metrics library. In Proceedings of the 32nd Case-Based Reasoning Research and Development (ICCBR), Merida, Mexico, 1–4 July 2024; Volume 14775, pp. 81–95. [Google Scholar]
- Darias, J.M.; Díaz-Agudo, B.; Recio-Garcia, J.A. A systematic review on model-agnostic XAI libraries. In Proceedings of the Workshops Proceedings for the 29th International Conference on Case-Based Reasoning (ICCBR 2021), Salamanca, Spain, 13–16 September 2021; Volume 3017, pp. 28–29. [Google Scholar]
- Leake, D.; Wilkerson, Z.; Vats, V.; Acharya, K.; Crandall, D. Examining the impact of network architecture on extracted feature quality for CBR. In Proceedings of the 31st Case-Based Reasoning Research and Development (ICCBR), Aberdeen, UK, 17–20 July 2023; Volume 14141, pp. 3–18. [Google Scholar]
Method | Element | Representation | Limitation | ||
---|---|---|---|---|---|
Frame | slot: facet: value | <Frame name> | Low reasoning efficiency; Hard to track and control. | ||
slot 1: | facet 11 | value 111, value 112, | |||
facet 1m | value 1m1, value 1m2, | ||||
slot n: | facet n1 | value n11, value n12, | |||
facet nm | value nm1, value nm2, | ||||
constraint: | constraint condition | ||||
Object-Oriented | CLASS::=<ID, DS, MS, MI> ID: Identifier DS: Data Structure MS: Method Set MI: Message Interface | class <name>[:<Superclass>] | |||
[<Class variable name>] | |||||
Structure | |||||
<Static structure description of an object> | |||||
Method | |||||
<Definition of an operation on an object> | |||||
Restraint | |||||
<Restricted condition> | |||||
END | |||||
Production Rule | <production>::= <precondition> <conclusion> | P→Q IF P THEN Q (CF = [0, 1]) CF: Certainty Factor | Low efficiency; Unable to express structured knowledge | ||
Semantic Nets | (Node1, Arc, Node2) Semantic Relation | AKO: A-Kind-Of | Non-rigidity; Low reasoning efficiency; Knowledge access complexity | ||
Predicate-Based | Predicate (Constant/Variate/Function) Conjunctions Quantifier | Predicate Formula | Cannot represent uncertain knowledge; Combinatorial explosion; Low efficiency |
Relation | Function | ||||
---|---|---|---|---|---|
⇔ | x and y are similar | ⇔ | x and y are exactly similar | ||
⇔ | x and y are dissimilar | ⇔ | x and y are exactly dissimilar | ||
⇔ | x is at least as similar to y as x to z | ⇔ | x and y are partly similar |
Deep Learning | CBR |
---|---|
Data, experience, and knowledge are all examples | Cases |
It is about learning knowledge | It is about learning knowledge |
General rules and laws are generated | Specific solutions are generated |
Technology | Methodology |
Unsupervised learning possible | Unsupervised problem solving cannot be done |
Eager learners | Lazy learners |
Results are not precise or certain | Results are not precise or certain |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yan, A.; Cheng, Z. A Review of the Development and Future Challenges of Case-Based Reasoning. Appl. Sci. 2024, 14, 7130. https://doi.org/10.3390/app14167130
Yan A, Cheng Z. A Review of the Development and Future Challenges of Case-Based Reasoning. Applied Sciences. 2024; 14(16):7130. https://doi.org/10.3390/app14167130
Chicago/Turabian StyleYan, Aijun, and Zijun Cheng. 2024. "A Review of the Development and Future Challenges of Case-Based Reasoning" Applied Sciences 14, no. 16: 7130. https://doi.org/10.3390/app14167130