[1]PAN Zhenghua,WANG Yong.Review of research on analogical reasoning in artificial intelligence[J].CAAI Transactions on Intelligent Systems,2023,18(4):643-661.[doi:10.11992/tis.202209002]
Copy

Review of research on analogical reasoning in artificial intelligence

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 643-661 Column: 综述 Public date: 2023-07-15
Title:
Review of research on analogical reasoning in artificial intelligence
Author(s):
PAN Zhenghua;  WANG Yong
School of Science, Jiangnan University, Wuxi 214122, China
Keywords:
artificial intelligence; analogical reasoning; objiect; target; mapping; computational model; case-based reasoning; deep learning
CLC:
TP18
DOI:
10.11992/tis.202209002
Abstract:
Analogical reasoning is a basic reasoning form in human thinking, and it is an important field in AI theory and technology research. The research of analogical reasoning in AI aims to carry out computational modeling in combination with the research of related disciplines, simulate and realize the the process of analogical reasoning on the computer, so as to generate an intelligent reasoning and learning system that can infer new knowledge. Since 1960s, the research on the theory and technology of analogical reasoning in AI has achieved a wealth of research results. Especially in recent years, the research combining analogical reasoning with deep learning has shown its importance in AI research. This paper aims to systematically summarize and review the main research and characteristics of analogical reasoning in AI from the past to the present. In this paper, based on a comprehensive survey of the retrieved more than 700 research documents related to analogical reasoning in AI, and a systematic analysis of 142 research documents, we believe that the analogical reasoning research in AI has shown different research characteristics in the last century and the present century, combined with our research, the analogical reasoning research in the two stages is summarized into 8 research topics such as “analogical problem solving”, “computational models” and “learning by analogy” and the basic research ideas, contents, characteristics and existing problems of these research topics and their representative research work are summarized and analysed. Finally, the future research direction and development trend of analogical reasoning in artificial intelligence are prospected.
References:
[1] POLYA G. How to solve it: A new aspect of mathematical method[M]. Princeton: Princeton University Press, 2004.
[2] HOFSTADTER D R, SANDER E. Surfaces and essences: analogy as the fuel and fire of thinking[M]. New York: Basic Books, 2013.
[3] KEDAR-CABELLI S T. Analogy-from a unified perspective. analogical reasoning[M]. Netherlands: Kluwer Academic Publishers. 1988: 65?103.
[4] GENTNER D, FORBUS K D. Computational models of analogy[J]. WIREs cognitive science, 2011, 2(3): 266–276.
[5] HALL R P. Computational approaches to analogical reasoning: a comparative analysis[J]. Artificial intelligence, 1989, 39(1): 39–120.
[6] STERNBERG R J. Component processes in analogical reasoning[J]. Psychological review, 1977, 84(4): 353–378.
[7] HOLYOAK K J, MORRISON R G. The Cambridge handbook of thinking and reasoning[M]. New York: Cambridge University Press, 2005.
[8] GENTNER D, MARAVILLA F. International handbook of thinking & reasoning[M]. New York: Psychology Press, 2018: 186?203.
[9] EVANS T G. A heuristic program to solve geometric-analogy problems[C]//Proceedings of Spring Joint Computer Conference. New York: ACM, 1964: 327?338.
[10] EVANS T G. A program for the solution of a class of geometric analogy intelligence test questions[M]. Semantic Information Processing. Cambridge: MIT Press, 1968: 271?353.
[11] KLING R E. Reasoning by analogy with applications to heuristic problem solving: a case study[R]. Tech. Rept. (CS-216). Palo Alto: Stanford University, 1971.
[12] KLING R E. A paradigm for reasoning by analogy[J]. Artificial intelligence, 1971, 2(2): 147–178.
[13] CARBONELL J G. A computational model of analogical problem solving[C]//The 7th International Joint Conference on Artificial Intelligence. Vancouver: International Joint Conferences on Artificial Intelligence Organization, 1981: 147?152
[14] CARBONELL J G. Experiential learning in analogical problem solving[C]//Proceedings of the Second AAAI Conference on Artificial Intelligence. New York: ACM, 1982: 168?171.
[15] CARBONELL J G. Learning by analogy: formulating and generalizing plans from past experience[M]. Machine Learning. Amsterdam: Elsevier, 1983: 137?161.
[16] CARBONELL J G. Derivational analogy and its role in problem solving[C]//The Third National Conference on Artificial Intelligence. Washington: AAAI Press, 1983: 64?69.
[17] CARBONELL J G. Derivational analogy: a theory of reconstructive problem solving and expertise acquisition[J]. Machine learning, 1986, 2: 371–392.
[18] WINSTON P H. Learning and reasoning by analogy[J]. Communications of the ACM, 1980, 23(12): 689–702.
[19] WINSTON P H. Learning new principles from precedents and exercises[J]. Artificial intelligence, 1982, 19(3): 321–350.
[20] WINSTON P H. Learning by augmenting rules and accumulating censors[J]. Machine learning: an artificial intelligence approach, 1986, 2: 45–61.
[21] WINSTON P H, BINFORD T O, KATZ B, et al. Learning physical descriptions from functional definitions, examples, and precedents[C]//Proceedings of the Third AAAI Conference on Artificial Intelligence. New York: ACM, 1983: 433?439.
[22] BURSTEIN M H. A model of learning in analogical problem solving[C]//The Third National Conference on Artificial Intelligence. Washington: AAAI Prese, 1983: 45?48.
[23] BURSTEIN M H. Concept formation by incremental analogical reasoning and debugging[J]. Machine learning:an artificial intelligence approach, 1986, 2: 351–369.
[24] BURSTEIN M H. Analogical learning with multiple models[J]. Machine learning, 1986, 2: 25–28.
[25] GREINER R. Learning by understanding analogies[J]. Artificial intelligence, 1988, 35(1): 81–125.
[26] GENTNER D. The structure of analogical models in science[R]. Tech. Rept. (No. 4451). Cambridge: [s.n.], 1980: 1.
[27] GENTNER D. Structure-mapping: a theoretical framework for analogy[J]. Cognitive science, 1983, 7(2): 155–170.
[28] MARKMAN A B, GENTNER D. Structure mapping in the comparison process[J]. The American journal of psychology, 2000, 113(4): 501.
[29] FALKENHAINER B, FORBUS K D, GENTNER D. The structure-mapping engine: algorithm and examples[J]. Artificial intelligence, 1989, 41(1): 1–63.
[30] FORBUS K D, GENTNER D, LAW K. MAC/FAC: a model of similarity-based retrieval[J]. Cognitive science, 1995, 19(2): 141–205.
[31] GICK M L, HOLYOAK K J. Schema induction and analogical transfer[J]. Cognitive psychology, 1983, 15(1): 1–38.
[32] KEITH J, HOLYOAK. The pragmatics of analogical transfer[J]. Psychology of learning and motivation, 1985, 19: 59–87.
[33] HOLYOAK K J, KOH K. Surface and structural similarity in analogical transfer[J]. Memory & cognition, 1987, 15(4): 332–340.
[34] KEITH J, HOLYOAK. Analogical mapping by constraint satisfaction[J]. Cognitive science, 1989, 13(3): 295–355.
[35] HOLYOAK K J, THAGARD P R. A computational model of analogical problem solving[M]. Similarity and Analogical Reasoning. Cambridge: Cambridge University Press, 1989: 242?266.
[36] THAGARD P, HOLYOAK K J, NELSON G, et al. Analog retrieval by constraint satisfaction[J]. Artificial intelligence, 1990, 46(3): 259–310.
[37] HELMAR, GUST. Metaphors and heuristic-driven theory projection (HDTP)[J]. Theoretical computer science, 2006, 354(1): 98–117.
[38] SCHWERING A, KRUMNACK U, KüHNBERGER K U, et al. Syntactic principles of heuristic-driven theory projection[J]. Cognitive systems research, 2009, 10(3): 251–269.
[39] SCHMIDT M, KRUMNACK U, GUST H, et al. Heuristic-driven theory projection: an overview[M]. Berlin: Springer Berlin Heidelberg, 2014: 163?194.
[40] MICLET L, PRADE H. Logical definition of analogical proportion and its fuzzy extensions[C]//2008 Annual Meeting of the North American Fuzzy Information Processing Society. Piscataway: IEEE, 2008: 1?6.
[41] MICLET L, PRADE H. Handling analogical proportions in classical logic and fuzzy logics settings[M].Berlin: Springer Berlin Heidelberg, 2009: 638?650.
[42] PRADE H, RICHARD G. Analogy, paralogy and reverse analogy: postulates and inferences[C]//Lecture Notes in Artificial Intelligence. Berlin: Springer, 2009: 306?314.
[43] PRADE H, RICHARD G. Analogical proportions: another logical view[C]//Research and Development in Intelligent Systems XXVI. London: Springer, 2009: 121?134.
[44] PRADE H, RICHARD G. Logical proportions - typology and roadmap[M]. Berlin: Springer Berlin Heidelberg, 2010: 757?767.
[45] PRADE H, RICHARD G. Multiple-valued logic interpretations of analogical, reverse analogical, and paralogical proportions[C]//2010 40th IEEE International Symposium on Multiple-Valued Logic. Piscataway: IEEE, 2010: 258?263.
[46] PRADE H, RICHARD G. Nonmonotonic features and uncertainty in reasoning with analogical proportions[C]//The 13th International Workshop on Non-Monotonic Reasoning. Toronto: IEEE, 2010: 14?16.
[47] PRADE H, RICHARD G. Reasoning with logical proportions[C]//The 12th International Conference on Principles of Knowledge Representation and Reasoning. Toronto: AAAI press, 2010: 545?555.
[48] MICLET L, PRADE H, GUENNEC D. Looking for analogical proportions in a formal concept analysis setting[C]//The 8th International Conference on Concept Lattices and their Applications. Nancy: IEEE, 2011: 295?307.
[49] PRADE H, RICHARD G. Analogy-making for solving IQ tests: a logical view[M]. Berlin: Springer Berlin Heidelberg, 2011: 241?257.
[50] PRADE H, SCHOCKAERT S. Completing rule bases in symbolic domains by analogy making[C]//Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology. Paris: Atlantis Press, 2011: 928?934.
[51] PRADE H, RICHARD G. Analogical proportions and multiple-valued logics[M]. Berlin: Springer Berlin Heidelberg, 2013: 497?509.
[52] PRADE H, RICHARD G. Homogeneous logical proportions: their uniqueness and their role in similarity-based prediction[C]//The 13th International Conference on Principles of Knowledge Representation and Reasoning. Rome: AAAI press, 2012. 402?412.
[53] PRADE H, RICHARD G, YAO Bing. Enforcing regularity by means of analogy-related proportions-a new approach to classification[J]. International journal of computer information systems and industrial management applications, 2012, 4: 648–658.
[54] PRADE H, RICHARD G. Picking the one that does not fit-A matter of logical proportions[C]//Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology. Paris: Atlantis Press, 2013: 392?399.
[55] SCHOCKAERT S, PRADE H. Completing symbolic rule bases using betweenness and analogical proportion[C]//Studies in Computational Intelligence. Berlin: Springer, 2014: 195?215.
[56] PRADE H, RICHARD G. From analogical proportion to logical proportions: a survey[M]. Computational Approaches to Analogical Reasoning: Current Trends. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014: 217?244.
[57] PRADE H, RICHARD G. Analogical proportions and analogical reasoning - an introduction[M]. Case-Based Reasoning Research and Development. Cham: Springer International Publishing, 2017: 16?32.
[58] PRADE H. Analogical proportions: from equality to inequality[J]. International journal of approximate reasoning, 2018, 101: 234–254.
[59] LIM S, PRADE H, RICHARD G. Solving word analogies: a machine learning perspective[M]. Cham: Springer International Publishing, 2019: 238?250.
[60] BARBOT N, MICLET L, PRADE H. Analogy between concepts[J]. Artificial intelligence, 2019, 275(C): 487–539.
[61] AFANTENOS S, KUNZE T, LIM S, et al. Analogies between sentences: theoretical aspects-preliminary experiments[M]. Cham: Springer International Publishing, 2021: 3?18.
[62] CHIU A, POUPART P, DIMARCO C. Generating lexical analogies using dependency relations[J]. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague: Association for Computational Linguistics, 2007: 561?570.
[63] TURNEY P D. A uniform approach to analogies, synonyms, antonyms, and associations[C]//Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1. New York: ACM, 2008: 905?912.
[64] TURNEY P D, LITTMAN M L. Corpus-based learning of analogies and semantic relations[J]. Machine learning, 2005, 60(1): 251–278.
[65] SPEER R, HAVASI C, LIEBERMAN H. Analogy space: reducing the dimensionality of common sense knowledge[C]//The 23rd National Conference on Artificial Intelligence. Chicago: AAAI Press, 2008: 548?553.
[66] VEALE T. An analogy-oriented type hierarchy for linguistic creativity[J]. Knowledge-based systems, 2006, 19(7): 471–479.
[67] VEALE T. Re-representation and creative analogy: a lexico-semantic perspective[J]. New generation computing, 2006, 24(3): 223–240.
[68] LANGLAIS P, PATRY A. Translating unknown words by analogical learning[C]//Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague: Association for Computational Linguistics, 2007: 877?886.
[69] BENAMARA F, TABOADA M, MATHIEU Y. Evaluative language beyond bags of words: linguistic insights and computational applications[J]. Computational linguistics, 2017, 43(1): 201–264.
[70] LEPAGE Y, DENOUAL E. Purest ever example-based machine translation: detailed presentation and assessment[J]. Machine translation, 2005, 19(3): 251–282.
[71] TURNEY P D. Similarity of semantic relations[J]. Computational linguistics, 2006, 32(3): 379–416.
[72] LIBERMAN H, KUMAR A. Providing expert advice by analogy for on-line help[C]//The 2005 IEEE/WIC/ACM International Conference on Web Intelligence. Piscataway: IEEE, 2005: 26?32.
[73] WANG S L, HONG T P, LIN Wenyang. Answering null queries by analogical reasoning on similarity-based fuzzy relational databases[J]. Journal of advanced computational intelligence and intelligent informatics, 2001, 5(3): 163–171.
[74] SIMOFF S, SIERRA C, LóPEZ DE MàNTARAS R. Mediation = information revelation + analogical reasoning[M]. Berlin: Springer Berlin Heidelberg, 2009: 145?160.
[75] STEIN S, LECHTHALER M, KRASSNITZER S, et al. Gear hobbing: a contribution to analogy testing and its wear mechanisms[J]. Procedia cirp, 2012, 1: 220–225.
[76] RAVEN J. The raven’s progressive matrices: change and stability over culture and time[J]. Cognitive psychology, 2000, 41(1): 1–48.
[77] RAGIN M, NEUBERT S. Solving Raven’s IQ-tests: an AI and cognitive modeling approach[C]//The 20th European Conference on Artificial Intelligence. Montpellier: IOS Press, 2012: 666?671.
[78] LOVETT A, FORBUS K D, USHER J. A structure- mapping model of Raven’s progressive matrices[C]//The 32nd Annual Conference of the Cognitive Science Society. Portland: Cognitive Science Society, 2010: 2761?2766.
[79] KLENK M, FORBUS K, TOMAI E, et al. Using analogical model formulation with sketches to solve bennett mechanical comprehension test problems[J]. Journal of experimental & theoretical artificial intelligence, 2011, 23(3): 299–327.
[80] BRECHER C, L?PENHAUS C, GRESCHERT R. Influence of the metalworking fluid on the running behavior of gear analogy test parts[J]. Production engineering, 2015, 9(3): 425–431.
[81] GALLASSI R, SAMBATI L, MASERATI M S, et al. Simple verbal analogies test: normative data on a short task exploring abstract thinking[J]. Aging clinical and experimental research, 2014, 26(1): 67–71.
[82] CURRIE A. Ethnographic analogy, the comparative method, and archaeological special pleading[J]. Studies in history and philosophy of science part A, 2016, 55: 84–94.
[83] JUDITH, CHARLIN. Testing an ethnographic analogy through geometric morphometrics: a comparison between ethnographic arrows and archaeological projectile points from Late Holocene Fuego-Patagonia[J]. Journal of anthropological archaeology, 2018, 51: 159–172.
[84] CROFT D, THAGARD P. Dynamic imagery: a computational model of motion and visual analogy[M]. Model-Based Reasoning. New York: Springer, 2002: 259?274.
[85] KUNDA M, MCGREGGOR K. A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations[J]. Cognitive systems research, 2013, 22/23: 47–66.
[86] DAVIES J, NERSESSIAN N J, GOEL A K. Visual models in analogical problem solving[J]. Foundations of science, 2005, 10(1): 133–152.
[87] CASAKIN H P, GOLDSCHMIDT G. Reasoning by visual analogy in design problem-solving: the role of guidance[J]. Environment and planning B:planning and design, 2000, 27(1): 105–119.
[88] HERTZMANN A, JACOBS C E, OLIVER N, et al. Image analogies[C]//Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 2001: 327?340.
[89] YANER P W, GOEL A K. Visual analogy: viewing analogical retrieval and mapping as constraint satisfaction problems[J]. Applied intelligence, 2006, 25(1): 91–105.
[90] DAVIES J, GOEL A K, NERSESSIAN N J. A cognitive model of visual analogical problem-solving transfer[C]//The Nineteenth International Joint Conference on Artificial Intelligence. Edinburgh: International Joint Conferences on Artificial Intelligence Organization, 2005: 1556?1557.
[91] O’DONOGHUE D P, BOHAN A, KEANE M T. Seeing things: inventive reasoning with geometric analogies and topographic maps[J]. New generation computing, 2006, 24(3): 267–288.
[92] PEDONE R, HUMMEL J E, HOLYOAK K J. The use of diagrams in analogical problem solving[J]. Memory & cognition, 2001, 29(2): 214–221.
[93] SCHWERING A, KüHNBERGER K U, KRUMNACK U, et al. Spatial cognition of geometric figures in the context of proportional analogies[M]. Berlin: Springer Berlin Heidelberg, 2009: 18?35.
[94] AZZEH M, NEAGU D, COWLING P. Improving analogy software effort estimation using fuzzy feature subset selection algorithm[C]//Proceedings of the 4th international workshop on Predictor models in software engineering. New York: ACM, 2008: 71?78.
[95] LI Jingzhou, RUHE G. A comparative study of attribute weighting heuristics for effort estimation by analogy[C]//Proceedings of the 2006 ACM/IEEE International Symposium on Empirical Software Engineering. New York: ACM, 2006: 66?74.
[96] PARITOSH P K, KLENK M E. Cognitive processes in quantitative estimation: analogical anchors and causal adjustment[C]//the 28th Annual Conference of the Cognitive Science Society. Hillsdale: Lawrence Erlbaum Associates, 2006: 1926–1931.
[97] SWAN J, KRAWIEC K. Discovering relational structure in program synthesis problems with analogical reasoning[M]. Genetic and Evolutionary Computation. Cham: Springer International Publishing, 2018: 149?164.
[98] SCHOCKAERT S, PRADE H. Interpolation and extrapolation in conceptual spaces: a case study in the music domain[M]. Berlin: Springer Berlin Heidelberg, 2011: 217?231.
[99] SCHOCKAERT S, PRADE H. Interpolative and extrapolative reasoning in propositional theories using qualitative knowledge about conceptual spaces[J]. Artificial intelligence, 2013, 202: 86–131.
[100] SOTOUDEH M, THAKUR A V. Analogy-making as a core primitive in the software engineering toolbox[C]//Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software. New York: ACM, 2020: 101?121.
[101] PEASE A, GUHE M, SMAILL A. Analogy formulation and modification in geometry[C]//The Second International Conference on Analogy. Sofia: NBU Press, 2009: 358?364.
[102] PEASE A, GUHE M, SMAILL A. Using analogies to find and evaluate mathematical conjectures[C]//The 1st International Conference on Computational Creativity. Lisbon, IEEE, 2010: 60-64.
[103] WHITTLE J, BUNDY A, BOULTON R. Proofs-as-programs as a framework for the design of an analogy-based ML editor[J]. Formal aspects of computing, 2002, 13(3/4/5): 403–421.
[104] VAMVAKOUSSI X. The use of analogies in mathematics instruction: affordances and challenges[M]//Cognitive Foundations for Improving Mathematical Learning. Amsterdam: Elsevier, 2019: 247?268.
[105] DANNY A J. GóMEZ R. Artificial mathematical intelligence: cognitive, (Meta)mathematical, Physical and Philosophical Foundations[M]. Cham: Springer International Publishing, 2020.
[106] AAMODT A, PLAZA E. Case-based reasoning: foundational issues, methodological variations, and system approaches[J]. AI communications, 1994, 7(1): 39–59.
[107] HüLLERMEIER E. Case-based approximate reasoning[M]. Dordrecht: Springer, 2007.
[108] CRAW S, WIRATUNGA N, ROWE R. Learning adaptation knowledge to improve case-based reasoning[J]. Artificial intelligence, 2006, 170(16/17): 1175–1192.
[109] D’AQUIN M, BADRA F, LAFROGNE S, et al. Case base mining for adaptation knowledge acquisition[C]//Proceedings of the 20th International Joint Conference on Artifical Intelligence. New York: ACM, 2007: 750?755.
[110] LIEBER J, NAUER E. Adaptation knowledge discovery using positive and negative cases[M]. Cham: Springer International Publishing, 2021: 140?155.
[111] D’AQUIN M, LIEBER J, NAPOLI A. Adaptation knowledge acquisition: a case study for case-based decision support in oncology[J]. Computational intelligence, 2006, 22(3/4): 161–176.
[112] GU Dongxiao. A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis[J]. Artificial intelligence in medicine, 2017, 77: 31–47.
[113] LAMY J B, SEKAR B, GUEZENNEC G, et al. Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach[J]. Artificial intelligence in medicine, 2019, 94: 42–53.
[114] BARTLETT C L, LIU Guanghui, BICHINDARITZ I. Classifying breast cancer tissue through DNA methylation and clinical covariate based retrieval[M]. Cham: Springer International Publishing, 2020: 82?96.
[115] OHANA B, DELANY S J, TIERNEY B. A case-based approach to cross domain sentiment classification[M]. Berlin: Springer Berlin Heidelberg, 2012: 284?296.
[116] ZHOU Feng, JIAO Jianxin, LINSEY J S. Latent customer needs elicitation by use case analogical reasoning from sentiment analysis of online product reviews[J]. Journal of mechanical design, 2015, 137(7): 071401.
[117] BERKA P. NEST: a compositional approach to rule-based and case-based reasoning[J]. Advances in artificial intelligence, 2011, 2011: 1–15.
[118] BERKA P. Sentiment analysis using rule-based and case-based reasoning[J]. Journal of intelligent information systems, 2020, 55(1): 51–66.
[119] LEE C H, CHEN C H, LI F, et al. Customized and knowledge-centric service design model integrating case-based reasoning and TRIZ[J]. Expert systems with applications, 2020, 143: 113062.
[120] LEAKE D, CRANDALL D. On bringing case-based reasoning methodology to deep learning[M]. Cham: Springer International Publishing, 2020: 343?348.
[121] LóPEZ-SáNCHEZ D, CORCHADO J M. A CBR system for image-based webpage classification: case representation with convolutional neural networks[C]// The Thirtieth International Florida Artificial Intelligence Research Society Conference. Marco Island: AAAI Press, 2017: 483-488.
[122] TURNER J T, FLOYD M W, GUPTA K M, et al. Novel object discovery using case-based reasoning and convolutional neural networks[M]. Cham: Springer International Publishing, 2018: 399?414.
[123] AMIN K, KAPETANAKIS S, ALTHOFF K D, et al. Answering with cases: a CBR approach to deep learning[M]. Cham: Springer International Publishing, 2018: 15?27.
[124] HOFFMANN M, MALBURG L, KLEIN P, et al. Using Siamese graph neural networks for similarity-based retrieval in process-oriented case-based reasoning[M]. Cham: Springer International Publishing, 2020: 229?244.
[125] HüLLERMEIER E. Towards analogy-based explanations in machine learning[M]. Cham: Springer International Publishing, 2020: 205?217.
[126] AGUDO B D. Mapping the challenges and opportunities of CBR for eXplainable AI[C]//Lecture Notes in Artificial Intelligence. Cham: Springe, 2019: I?II
[127] KEANE M T, KENNY E M. How case-based reasoning explains neural networks: a theoretical analysis of XAI using Post-Hoc explanation-by-example from a survey of ANN-CBR twin-systems[C]//Case-Based Reasoning Research and Development: 27th International Conference. New York: ACM, 2019: 155?171.
[128] NADEEM R, WU Huijun, PAIK H Y, et al. A case based deep neural network interpretability framework and its user study[M]. Cham: Springer International Publishing, 2019: 147?161.
[129] RECIO-GARCí-A J A, Dí-AZ-AGUDO B, PINO-CASTILLA V. CBR-LIME: a case-based reasoning approach to provide specific local interpretable model-agnostic explanations[M]. Cham: Springer International Publishing, 2020: 179?194.
[130] LI O, LIU Hao, CHEN Chaofan, et al. Deep learning for case-based reasoning through prototypes: a neural network that explains its predictions[C]//the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans: AAAI Press, 2018: 3530–3537.
[131] WEBER R O, JOHS A J, LI Jianfei, et al. Investigating textual case-based XAI[M]. Case-Based Reasoning Research and Development. Cham: Springer International Publishing, 2018: 431?447.
[132] LAWRENCE G, CALEB K, DAVID L. CBR confidence as a basis for confidence in black box systems[M].Cham: Springe, 2019: 95?109.
[133] KEANE M T, SMYTH B. Good counterfactuals and where to find them: a case-based technique for generating counterfactuals for explainable AI (XAI)[EB/OL]. (2020-05-26)[2022-09-01]. https://arxiv.org/abs/2005.13997.
[134] TEMRAZ M, KENNY E M, RUELLE E, et al. Handling climate change using counterfactuals: using counterfactuals in data augmentation to predict crop growth in an uncertain climate future[M]. Cham: Springer International Publishing, 2021: 216?231.
[135] CHEN X J, JIA S B, YANG X. A review: knowledge reasoning over knowledge graph[J]. Expert systems with applications, 2020, 141: 112948.
[136] 官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展[J]. 软件学报, 2018, 29(10): 2966–2994
[137] 李波, 赵沁平. 基于突出特征的类比联想[J]. 计算机学报, 1994, 17(9): 690–696
LI Bo, ZHAO Qinpin. Salient feature-based analogical reminding[J]. Chinese journal of computers, 1994, 17(9): 690–696
[138] 李波, 赵沁平. 基于相关性的类比求精[J]. 计算机学报, 1995, 18(3): 231–235
LI Bo, ZHAO Qinping. Relevance-directed analogical elaboration[J]. Chinese journal of computers, 1995, 18(3): 231–235
[139] 李波. 类比推理系统[J]. 计算机学报, 1995, 18(3): 231–235
LI Bo. BHARS: an analogical reasoning system[J]. Chinese journal of computers, 1995, 18(3): 231–235
[140] 赵沁平, 李波. 类比推理研究中的几个问题[J]. 模式识别与人工智能, 1995, 8(S1): 48–58
ZHAO Qinping, LI Bo. Discussion about several problems of analogical reasoning[J]. Pattern recognition and artificial intelligence, 1995, 8(S1): 48–58
[141] 李波, 罗玉龙, 赵沁平. 一种类比匹配原理及其实现[J]. 软件学报, 1996, 6(1): 8–16
LI Bo, LUO Yulong, ZHAO Qinping. An theory of analogical match and its implementation[J]. Journal of software, 1996, 6(1): 8–16
[142] 赵沁平, 李波. 类比推理的计算模型[J]. 软件学报, 1996, 7(3): 156–162
ZHAO Qinping, LI Bo. A computational model of analogical reasoning[J]. Journal of software, 1996, 7(3): 156–162
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems