Relevant bibliographies by topics / Similarity-based Reasoning

Academic literature on the topic 'Similarity-based Reasoning'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Similarity-based Reasoning"

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Sun, Ron. "Robust reasoning: integrating rule-based and similarity-based reasoning." Artificial Intelligence 75, no. 2 (June 1995): 241–95. http://dx.doi.org/10.1016/0004-3702(94)00028-y.

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Mondal, Banibrata, and Swapan Raha. "Similarity-Based Inverse Approximate Reasoning." IEEE Transactions on Fuzzy Systems 19, no. 6 (December 2011): 1058–71. http://dx.doi.org/10.1109/tfuzz.2011.2159981.

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Biacino, Loredana, Giangiacomo Gerla, and Mingsheng Ying. "Approximate Reasoning Based on Similarity." MLQ 46, no. 1 (January 2000): 77–86. http://dx.doi.org/10.1002/(sici)1521-3870(200001)46:1<77::aid-malq77>3.0.co;2-x.

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Luo, Minxia, and Ruirui Zhao. "Fuzzy reasoning algorithms based on similarity." Journal of Intelligent & Fuzzy Systems 34, no. 1 (January 12, 2018): 213–19. http://dx.doi.org/10.3233/jifs-171140.

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Hüllermeier, Eyke. "Similarity-based inference as evidential reasoning." International Journal of Approximate Reasoning 26, no. 2 (February 2001): 67–100. http://dx.doi.org/10.1016/s0888-613x(00)00062-1.

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Raha, Swapan, Abul Hossain, and Sujata Ghosh. "Similarity based approximate reasoning: fuzzy control." Journal of Applied Logic 6, no. 1 (March 2008): 47–71. http://dx.doi.org/10.1016/j.jal.2007.01.001.

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Whitaker, Leslie A., Richard H. Stottler, and James A. King. "Case-Based Reasoning: Taming the Similarity Heuristic." Proceedings of the Human Factors Society Annual Meeting 34, no. 4 (October 1990): 312–15. http://dx.doi.org/10.1177/154193129003400416.

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Raha, S., N. R. Pal, and K. S. Ray. "Similarity-based approximate reasoning: methodology and application." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, no. 4 (July 2002): 541–47. http://dx.doi.org/10.1109/tsmca.2002.804787.

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Sessa, Maria I. "Approximate reasoning by similarity-based SLD resolution." Theoretical Computer Science 275, no. 1-2 (March 2002): 389–426. http://dx.doi.org/10.1016/s0304-3975(01)00188-8.

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Esteva, Francesc, Pere Garcia, Lluís Godo, and Ricardo Rodríguez. "A modal account of similarity-based reasoning." International Journal of Approximate Reasoning 16, no. 3-4 (April 1997): 235–60. http://dx.doi.org/10.1016/s0888-613x(96)00126-0.

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Dissertations / Theses on the topic "Similarity-based Reasoning"

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Jurisica, Igor. "TA3, theory, implementation, and applications of similarity-based retrieval for case-based reasoning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35199.pdf.

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Galushka, Mykola. "Discovering and managing similarity knowledge in temporal case-based reasoning systems." Thesis, University of Ulster, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535142.

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Wagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
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Wagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
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Steffens, Timo. "Enhancing similarity measures with imperfect rule-based background knowledge." Doctoral thesis, Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2898562&prov=M&dok_var=1&dok_ext=htm.

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El-Mehalawi, Mohamed. "A geometric similarity case-based reasoning system for cost estimation in net-shape manufacturing /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488186329504367.

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Wolf, Markus Adrian. "Applying case based reasoning and structural similarity for effective retrieval of expert knowledge from software designs." Thesis, University of Greenwich, 2012. http://gala.gre.ac.uk/11978/.

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Due to the proliferation of object-oriented software development, UML software designs are ubiquitous. The creation of software designs already enjoys wide software support through CASE (Computer-Aided Software Engineering) tools. However, there has been limited application of computer reasoning to software designs in other areas. Yet there is expert knowledge embedded in software design artefacts which could be useful if it were successfully retrieved. Thus, there is a need for automated support for expert knowledge retrieval from software design artefacts. A software design is an abstract representation of a software product and, in the case of a class diagram, contains information about its structure. It is therefore possible to extract knowledge about a software application from its design. For a human expert an important aspect of a class diagram are the semantic tags associated with each composing element, as these provide a link to the concept each element represents. For implemented code, however, the semantic tags have no bearing. The focus of this research has been on the question of whether is it possible to retrieve knowledge from class diagrams in the absence of semantic information. This thesis formulates an approach which combines case-based reasoning with graph matching to retrieve knowledge from class diagrams using only structural information. The practical applicability of this research has been demonstrated in the areas of cost estimation and plagiarism detection. It was shown that by applying case-based reasoning and graph matching to measure similarity between class diagrams it is possible to identify properties of an implementation not encoded within the actual diagram, such as the domain, programming language, quality and implementation cost. An approach for increasing users’ confidence in automatic class diagram matching by providing explanation is also presented. The findings show that the technique applied here can contribute to industry and academia alike in obtaining solutions from class diagrams where semantic information is lacking. The approach presented here, as well as its evaluation, were automated through the development of the UMLSimilator software tool.
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Nordström, Markus. "Automatic Source Code Classification : Classifying Source Code for a Case-Based Reasoning System." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25519.

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This work has investigated the possibility of classifying Java source code into cases for a case-based reasoning system. A Case-Based Reasoning system is a problem solving method in Artificial Intelligence that uses knowledge of previously solved problems to solve new problems. A case in case-based reasoning consists of two parts: the problem part and solution part. The problem part describes a problem that needs to be solved and the solution part describes how this problem was solved. In this work, the problem is described as a Java source file using words that describes the content in the source file and the solution is a classification of the source file along with the source code. To classify Java source code, a classification system was developed. It consists of four analyzers: type filter, documentation analyzer, syntactic analyzer and semantic analyzer. The type filter determines if a Java source file contains a class or interface. The documentation analyzer determines the level of documentation in asource file to see the usefulness of a file. The syntactic analyzer extracts statistics from the source code to be used for similarity, and the semantic analyzer extracts semantics from the source code. The finished classification system is formed as a kd-tree, where the leaf nodes contains the classified source files i.e. the cases. Furthermore, a vocabulary was developed to contain the domain knowledge about the Java language. The resulting kd-tree was found to be imbalanced when tested, as the majority of source files analyzed were placed inthe left-most leaf nodes. The conclusion from this was that using documentation as a part of the classification made the tree imbalanced and thus another way has to be found. This is due to the fact that source code is not documented to such an extent that it would be useful for this purpose.
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Qvarford, Johannes. "EN SCHACK AI BASERAD PÅ CASE-BASED REASONING MED GRUNDLIG LIKHET." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11049.

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Schack är ett spel som ofta används för att undersöka olika tekniker inom artificiell intelligens (AI). I det här arbetet ställs frågan om det går att utveckla en AI-agent vars beslutsfattande är baserat på tekniken Case-based Reasoning (CBR) med grundlig likhet som spelar bättre med fallbaser baserade på bättre experter. En AI-agent har utvecklats som spelat ett antal partier mot sig själv med olika fallbaser baserade på olika experter. Efter att ha undersökt resultatet visade de sig att AI-agenten spelar så dåligt att den nästan aldrig lyckades vinna oavsett fallbas, vilket gjorde att det inte gick att rangordna dem efter skicklighet. I framtida arbete är det intressant att undersöka andra likheter än grundlig likhet. Det är även av intresse att undersöka om en CBR-baserad schackspelande AI-agent kan spela schack med hög skicklighet.
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Saeed, Soran. "An investigation into establishing a generalised approach for defining similarity metrics between 3D shapes for the casting design problem in case-based reasoning (CBR)." Thesis, University of Greenwich, 2006. http://gala.gre.ac.uk/6288/.

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This thesis investigates the feasibility of establishing a generalised approach for defining similarity metrics between 3D shapes for the casting design problem in Case-Based Reasoning (CBR). This research investigates a new approach for improving the quality of casting design advice achieved from a CBR system using casting design knowledge associated with past cases. The new approach uses enhanced similarity metrics to those used in previous research in this area to achieve improvements in the advice given. The new similarity metrics proposed here are based on the decomposition of casting shape cases into a set of components. The research into metrics defines and uses the Component Type Similarity Metric (CTM) and Maximum Common Subgraph (MCS) metric between graph representations of the case shapes and are focused on the definition of partial similarity between the components of the same type that take into account the geometrical features and proportions of each single shape component. Additionally, the investigation extends the scope of the research to 3D shapes by defining and evaluating a new metric for the overall similarity between 3D shapes. Additionally, this research investigates a methodology for the integration of the CBR cycle and automation of the feature extraction from target and source case shapes. The ShapeCBR system has been developed to demonstrate the feasibility of integrating the CBR approach for retrieving and reusing casting design advice. The ShapeCBR system automates the decomposition process, the classification process and the shape matching process and is used to evaluate the new similarity metrics proposed in this research and the extension of the approach to 3D shapes. Evaluation of the new similarity metrics show that the efficiency of the system is enhanced using the new similarity metrics and that the new approach provides useful casting design information for 3D casting shapes. Additionally, ShapeCBR shows that it is possible to automate the decomposition and classification of components that allow a case shape to be represented in graph form and thus provide the basis for automating the overall CBR cycle. The thesis concludes with new research questions that emerge from this research and an agenda for further work to be pursued in further research in the area.
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Books on the topic "Similarity-based Reasoning"

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Dubois, Didier. Raisonnement approché basé sur la similarité =: Similarity-based approximate reasoning. Toulouse: Institut de Recherche en Informatique de Toulouse, 1994.

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Steffens, Timo. Enhancing similarity measures with imperfect rule-based background knowledge. Berlin: Akademische Verlagsgesellschaft Aka, 2006.

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Jurisica, Igor. TA3: Theory, implementation, and applications of similarity-based retrieval for case-based reasoning. 1998.

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Steffens, Timo. Enhancing Similarity Measures with Imperfect Rule-Based Background Knowledge: Volume 302 Dissertations in Artificial Intelligence - Infix (Diski: Dissertationen ... Dissertationen Zur Kunstlichen Intelligenz). IOS Press, 2006.

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Book chapters on the topic "Similarity-based Reasoning"

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Arcos, Josep LLuís. "Music and Similarity Based Reasoning." In Soft Computing in Humanities and Social Sciences, 467–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24672-2_24.

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Sebag, Michèle, and Marc Schoenauer. "A rule-based similarity measure." In Topics in Case-Based Reasoning, 119–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58330-0_81.

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Yu, Qiming. "Model-Based Reasoning and Similarity in the World." In Model-Based Reasoning, 275–85. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0605-8_16.

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Dubois, D., F. Esteva, P. Garcia, L. Godo, and H. Prade. "Similarity-based consequence relations." In Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 171–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60112-0_20.

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Steffens, Timo. "Knowledge-Rich Similarity-Based Classification." In Case-Based Reasoning Research and Development, 522–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11536406_40.

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Richter, Michael M., and Stefan Wess. "Similarity, Uncertainty and Case-Based Reasoning in Patdex." In Automated Reasoning Series, 249–65. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3488-0_12.

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Mondal, Banibrata, and Swapan Raha. "Fuzzy Resolution with Similarity-Based Reasoning." In Studies in Fuzziness and Soft Computing, 361–78. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06323-2_23.

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Su, Chang, Changle Zhou, and Yijiang Chen. "Cognitive Metaphor-Based Fuzzy Similarity Reasoning." In Communications in Computer and Information Science, 143–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23226-8_19.

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Shapiro, L. G., I. Atmosukarto, H. Cho, H. J. Lin, S. Ruiz-Correa, and J. Yuen. "Similarity-Based Retrieval for Biomedical Applications." In Case-Based Reasoning on Images and Signals, 355–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73180-1_12.

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Börner, Katy. "Structural similarity as guidance in case-based design." In Topics in Case-Based Reasoning, 197–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58330-0_87.

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Conference papers on the topic "Similarity-based Reasoning"

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Bin He, Yan Qiu, and Xiaoyin Chen. "Problem-Based Similarity Innovation Reasoning." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1712829.

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Caballero, Rafael, Mario Rodríguez-Artalejo, and Carlos A. Romero-Díaz. "Similarity-based reasoning in qualified logic programming." In the 10th international ACM SIGPLAN symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389449.1389472.

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Wang, Lu, Binbin Xue, and Keyun Qin. "A similarity-based fuzzy soft reasoning method." In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2017. http://dx.doi.org/10.1109/iske.2017.8258817.

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Wu, Dongrui, and Jerry M. Mendel. "Similarity-based perceptual reasoning for perceptual computing." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277374.

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Mekik, Can Serif, Ron Sun, and David Yun Dai. "Similarity-Based Reasoning, Raven's Matrices, and General Intelligence." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/218.

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This paper presents a model tackling a variant of the Raven's Matrices family of human intelligence tests along with computational experiments. Raven's Matrices are thought to challenge human subjects' ability to generalize knowledge and deal with novel situations. We investigate how a generic ability to quickly and accurately generalize knowledge can be succinctly captured by a computational system. This work is distinct from other prominent attempts to deal with the task in terms of adopting a generalized similarity-based approach. Raven's Matrices appear to primarily require similarity-based or analogical reasoning over a set of varied visual stimuli. The similarity-based approach eliminates the need for structure mapping as emphasized in many existing analogical reasoning systems. Instead, it relies on feature-based processing with both relational and non-relational features. Preliminary experimental results suggest that our approach performs comparably to existing symbolic analogy-based models.
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Dvořák, Antonín, Balasubramaniam Jayaram, and Martin Štěpnička. "Similarity-based Reasoning from the Perspective of Extensionality." In 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/asum.k.210827.043.

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Vargas, Juan E., J. R. Bourne, A. J, Brodersen, Martin Hofmann, and G. C. Collins. "Similarity-based reasoning about diagnosis of analog circuits." In the first international conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/51909.51920.

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Masood, Mona, and Nur Azlina Mohamed Mokmin. "Learning material recommendation based on case-based reasoning similarity scores." In THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17). Author(s), 2017. http://dx.doi.org/10.1063/1.5005421.

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Lee, Chun Kyung, Thi Hien Pham, Hee Seong Kim, and Hee Yong Youn. "Similarity Based Distributed Context Reasoning with Layer Context Modeling." In 2011 IEEE 35th Annual Computer Software and Applications Conference - COMPSAC 2011. IEEE, 2011. http://dx.doi.org/10.1109/compsac.2011.49.

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Mandal, Sayantan, and Balasubramaniam Jayaram. "Interpolativity and Continuity of Similarity-Based Reasoning Fuzzy Inference." In 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/asum.k.210827.047.

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