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Relevant bibliographies by topics / Reasoning Methods

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Academic literature on the topic 'Reasoning Methods'

Author: Grafiati

Published: 2 June 2025

Last updated: 22 June 2025

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Journal articles on the topic "Reasoning Methods"

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Perovic, Aleksandar, Nedeljko Stefanovic, Milos Milosevic, and Dejan Ilic. "Automated reasoning-alternative methods." Serbian Journal of Electrical Engineering 1, no. 3 (2004): 15–20. http://dx.doi.org/10.2298/sjee0403015p.

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Our main goal is to describe a potential usage of the interpretation method (i.e. formal representation of one first order theory into another) together with quantifier elimination procedures developed in the GIS.

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Daniel, Michelle, Joseph Rencic, Steven J. Durning, et al. "Clinical Reasoning Assessment Methods." Academic Medicine 94, no. 6 (2019): 902–12. http://dx.doi.org/10.1097/acm.0000000000002618.

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Andrews, Douglas M., Richard A. Johnson, and Kam-Wah Tsui. "Statistical Reasoning and Methods." American Statistician 53, no. 3 (1999): 291. http://dx.doi.org/10.2307/2686113.

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Hofbauer, M., and N. Dourdoumas. "Lyapunov based reasoning methods." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 31, no. 6 (2001): 546–58. http://dx.doi.org/10.1109/3468.983412.

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Lordo, Robert A. "Statistical Reasoning and Methods." Technometrics 41, no. 3 (1999): 269–70. http://dx.doi.org/10.1080/00401706.1999.10485687.

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Long, William J. "Medical informatics: reasoning methods." Artificial Intelligence in Medicine 23, no. 1 (2001): 71–87. http://dx.doi.org/10.1016/s0933-3657(01)00076-8.

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Buchberger, B., G. E. Collins, and B. Kutzler. "Algebraic Methods for Geometric Reasoning." Annual Review of Computer Science 3, no. 1 (1988): 85–119. http://dx.doi.org/10.1146/annurev.cs.03.060188.000505.

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Baldamus, W. "Understanding Habermas's methods of reasoning." History of the Human Sciences 5, no. 2 (1992): 97–115. http://dx.doi.org/10.1177/095269519200500206.

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Van Dyke Parunak, H., Sven A. Brueckner, Robert Matthews, and John Sauter. "Swarming methods for geospatial reasoning." International Journal of Geographical Information Science 20, no. 9 (2006): 945–64. http://dx.doi.org/10.1080/13658810600830525.

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Snow, Paul. "Nearly Bayesian uncertain reasoning methods." Behavioral and Brain Sciences 20, no. 4 (1997): 779–80. http://dx.doi.org/10.1017/s0140525x97281641.

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Subjects are reported as being somewhat Bayesian, but as violating the normative ideal on occasion. To abjure probability altogether is difficult. To use Bayes' Theorem scrupulously when weighing evidence can incur costs without corresponding benefits. The subjects' evident nuanced probabilism appears both realistic and reasonable.

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Dissertations / Theses on the topic "Reasoning Methods"

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Westphal, Matthias [Verfasser], and Bernhard [Akademischer Betreuer] Nebel. "Qualitative constraint-based reasoning: methods and applications." Freiburg : Universität, 2015. http://d-nb.info/1119805627/34.

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Berger, Daniel Robert Howard James. "Improving legal reasoning using Bayesian probability methods." Thesis, Queen Mary, University of London, 2015. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8914.

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A thesis which explores the possibility of introducing Bayesian probability methods into the criminal justice system, and in doing so, exposing and eradicating some common fallacies. This exposure aims to reduce miscarriages of justice by illustrating that some evidence routinely relied upon by the prosecution, may not have as high a probative value towards its ultimate hypothesis of ‘guilt’ as has been traditionally thought and accepted.

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Xia, Qijun. "Hybrid reasoning methods for intelligent operation support systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0017/NQ46948.pdf.

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Letier, Emmanuel. "Reasoning about Agents in Goal-Oriented Requirements Engineering." Université catholique de Louvain, 2002. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-11042002-163458/.

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The thesis proposes a number of techniques for elaborating requirements constructively from high-level goals. The techniques are based on the KAOS goal-oriented method for requirements engineering. This method consists in identifying goals and refining them into subgoals until the latter can be assigned as responsibilities of single agents such as humans, devices and software. Domain properties and assumptions about the software environment are also used during the goal refinement process. The method supports the exploration of alternative goal refinements and alternative responsibility assignments of goals to agents. It also supports the identification and resolution of conflicts between goals, and the identification and resolution of exceptional agent behaviors, called obstacles, that violate goals and assumptions produced during the goal refinement process. The thesis enriches the KAOS framework through three kinds of techniques: (a) techniques for identifying agents, goal refinements, and alternative responsibility assignments, and for deriving agent interfaces from such responsibility assignments; (b) techniques for deriving operational requirements from goal specifications; (c) techniques for generating obstacles to the satisfaction of idealized goals and assumptions, and for generating alternative obstacle resolutions. The result is a coherent body of systematic techniques for requirements elaboration that are both theoretically well-founded (a formal model of agent is defined) and effective in practice (the techniques are validated on two real case studies of significant size: the London ambulance despatching system, and the Bay Area Rapid Transit train system).

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Lu, Ling, and Bofeng Li. "Combining Different Feature Weighting Methods for Case Based Reasoning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26603.

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Arens, Sheila A. "A study of evaluative reasoning in evaluative studies judged "outstanding"." [Bloomington, Ind.] : Indiana University, 2005. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3183491.

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Thesis (Ph.D.)--Indiana University, School of Education, 2005.<br>Source: Dissertation Abstracts International, Volume: 66-08, Section: A, page: 3120. Adviser: Ginette Delandshere. Title from dissertation home page (viewed Oct. 9, 2006).

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Mallory, Richard Smith. "Tools for explaining complex qualitative simulations /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Lagerkvist, Mikael Zayenz. "Machine Assisted Reasoning for Multi-Threaded Java Bytecode." Thesis, KTH, Electronic, Computer and Software Systems, ECS, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9512.

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<p>In this thesis an operational semantics for a subset of the Java Virtual Machine (JVM) is developed and presented. The subset contains standard operations such as control flow, computation, and memory management. In addition, the subset contains a treatment of parallel threads of execution.</p><p> </p><p>The operational semantics are embedded into a $µ$-calculus based proof assistant, called the VeriCode Proof Tool (VCPT). VCPT has been developed at the Swedish Institute of Computer Science (SICS), and has powerful features for proving inductive assertions.</p><p> </p><p>Some examples of proving properties of programs using the embedding are presented.</p><br><p>I det här examensarbetet presenteras en operationell semantik för en delmängd av Javas virtuella maskin. Den delmängd som hanteras innehåller kontrollflöde, beräkningar och minneshantering. Vidare beskrivs semantiken för parallella exekveringstrådar.</p><p>Den operationella semantiken formaliseras i en bevisassistent for $µ$-kalkyl, VeriCode Proof Tool (VCPT). VCPT har utvecklats vid Swedish Institiute of Computer Science (SICS), och har kraftfulla tekniker för att bevisa induktiva påståenden.</p><p>Några exempel på bevis av egenskaper hos program användandes formaliseringen presenteras också.</p>

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Castellini, Claudio. "Automated reasoning in quantified modal and temporal logics." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/753.

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This thesis is about automated reasoning in quantified modal and temporal logics, with an application to formal methods. Quantified modal and temporal logics are extensions of classical first-order logic in which the notion of truth is extended to take into account its necessity or equivalently, in the temporal setting, its persistence through time. Due to their high complexity, these logics are less widely known and studied than their propositional counterparts. Moreover, little so far is known about their mechanisability and usefulness for formal methods. The relevant contributions of this thesis are threefold: firstly, we devise a sound and complete set of sequent calculi for quantified modal logics; secondly, we extend the approach to the quantified temporal logic of linear, discrete time and develop a framework for doing automated reasoning via Proof Planning in it; thirdly, we show a set of experimental results obtained by applying the framework to the problem of Feature Interactions in telecommunication systems. These results indicate that (a) the problem can be concisely and effectively modeled in the aforementioned logic, (b) proof planning actually captures common structures in the related proofs, and (c) the approach is viable also from the point of view of efficiency.

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Sticksel, Christoph. "Efficient equational reasoning for the Inst-Gen Framework." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/efficient-equational-reasoning-for-the-instgen-framework(67ead780-3ec9-4897-ac17-48d3de011b4b).html.

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We can classify several quite different calculi for automated reasoning in first-order logic as instantiation-based methods (IMs). Broadly speaking, unlike in traditional calculi such as resolution where the first-order satisfiability problem is tackled by deriving logical conclusions, IMs attempt to reduce the first-order satisfiability problem to propositional satisfiability by intelligently instantiating clauses. The Inst-Gen-Eq method is an instantiation-based calculus which is complete for first-order clause logic modulo equality. Its distinctive feature is that it combines first-order reasoning with efficient ground satisfiability checking, which is delegated in a modular way to any state-of-the-art ground solver for satisfiability modulo theories (SMT). The first-order reasoning modulo equality employs a superposition-style calculus which generates the instances needed by the ground solver to refine a model of a ground abstraction or to witness unsatisfiability. The thesis addresses the main issue in the Inst-Gen-Eq method, namely efficient extraction of instances, while providing powerful redundancy elimination techniques. To that end we introduce a novel labelled unit superposition calculus with sets, AND/OR trees and ordered binary decision diagrams (OBDDs) as labels. The different label structures permit redundancy elimination each to a different extent. We prove completeness of redundancy elimination from labels and further integrate simplification inferences based on term rewriting. All presented approaches, in particular the three labelled calculi are implemented in the iProver-Eq system and evaluated on standard benchmark problems.

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Books on the topic "Reasoning Methods"

1

Johnson, Richard Arnold. Statistical reasoning and methods. John Wiley, 2004.

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1947-, Tsui Kam-Wah, ed. Statistical reasoning and methods. John Wiley, 1997.

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Song, Xiping. Software design methods: Analysis and reasoning. Oxford University Press, 1997.

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Baggini, Julian. The philosopher's toolkit: A compendium of philosophical concepts and methods. 2nd ed. Wiley-Blackwell, 2010.

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Shavelson, Richard J. Statistical reasoning for the behavioral sciences. 2nd ed. Allyn and Bacon, 1988.

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Shavelson, Richard J. Statistical reasoning for the behavioral sciences. 3rd ed. Allyn and Bacon, 1996.

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Das, Anupam, and Sara Negri, eds. Automated Reasoning with Analytic Tableaux and Related Methods. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86059-2.

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Brünnler, Kai, and George Metcalfe, eds. Automated Reasoning with Analytic Tableaux and Related Methods. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22119-4.

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Schmidt, Renate A., and Cláudia Nalon, eds. Automated Reasoning with Analytic Tableaux and Related Methods. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66902-1.

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Olivetti, Nicola, ed. Automated Reasoning with Analytic Tableaux and Related Methods. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73099-6.

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Book chapters on the topic "Reasoning Methods"

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Chapman, Nick, Simon Finn, and Michael P. Fourman. "Reasoning." In Practical Formal Methods for Hardware Design. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60641-0_10.

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Weispfenning, Volker. "Solving Constraints by Elimination Methods." In Automated Reasoning. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25984-8_25.

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Ridderikhoff, J. "Ways of reasoning." In Methods in Medicine. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1097-3_2.

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Niskanen, Vesa A. "Approximate Reasoning." In Soft Computing Methods in Human Sciences. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-36421-4_5.

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Hendrickson, Noel. "Applied Counterfactual Reasoning." In Computational Methods for Counterterrorism. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01141-2_13.

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Bibel, Wolfgang. "Methods of Automated Reasoning." In Fundamentals of Artificial Intelligence. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-662-40145-3_5.

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Bibel, Wolfgang. "Methods of automated reasoning." In Fundamentals of Artificial Intelligence. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0022683.

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Richter, Michael M., and Rosina O. Weber. "Basic Formal Definitions and Methods." In Case-Based Reasoning. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40167-1_22.

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Furche, Tim, Benedikt Linse, François Bry, Dimitris Plexousakis, and Georg Gottlob. "RDF Querying: Language Constructs and Evaluation Methods Compared." In Reasoning Web. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11837787_1.

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Antonsen, Roger. "Chapter 16 Reasoning About Models." In Logical Methods. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63777-4_17.

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Conference papers on the topic "Reasoning Methods"

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Chen, Shuguang, and Guang Lin. "LLM Reasoning Engine: Specialized Training for Enhanced Mathematical Reasoning." In Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.knowledgenlp-1.9.

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Wang, Junlin, Siddhartha Jain, Dejiao Zhang, Baishakhi Ray, Varun Kumar, and Ben Athiwaratkun. "Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.1112.

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Liao, Yuan-Hong, Rafid Mahmood, Sanja Fidler, and David Acuna. "Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.947.

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Mehrafarin, Houman, Arash Eshghi, and Ioannis Konstas. "Reasoning or a Semblance of it? A Diagnostic Study of Transitive Reasoning in LLMs." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.650.

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Sizov, Gleb, and Pinar Öztürk. "Automatic Extraction of Reasoning Chains from Textual Reports." In Proceedings of TextGraphs-8 Graph-based Methods for Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/w13-5009.

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Zheng, Zi’ou, Christopher Malon, Martin Renqiang Min, and Xiaodan Zhu. "Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.854.

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Valentino, Marco, and André Freitas. "Introductory Tutorial: Reasoning with Natural Language Explanations." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-tutorials.4.

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Feng, Tao, Yicheng Li, Li Chenglin, Hao Chen, Fei Yu, and Yin Zhang. "Teaching Small Language Models Reasoning through Counterfactual Distillation." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.333.

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Purohit, Kiran, Venktesh V, Raghuram Devalla, Krishna Mohan Yerragorla, Sourangshu Bhattacharya, and Avishek Anand. "EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.307.

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Schwartz, Eli, Leshem Choshen, Joseph Shtok, Sivan Doveh, Leonid Karlinsky, and Assaf Arbelle. "NumeroLogic: Number Encoding for Enhanced LLMs’ Numerical Reasoning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.12.

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Reports on the topic "Reasoning Methods"

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Ruspini, Enrique H. Advanced Methods of Approximate Reasoning. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada232140.

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Ruspini, Enrique H. Advanced Concepts and Methods of Approximate Reasoning. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada216474.

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Ludwig, Michel, and Rafael Peñaloza. Error-Tolerant Reasoning in the Description Logic EL. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.209.

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Developing and maintaining ontologies is an expensive and error-prone task. After an error is detected, users may have to wait for a long time before a corrected version of the ontology is available. In the meantime, one might still want to derive meaningful knowledge from the ontology, while avoiding the known errors. We study error-tolerant reasoning tasks in the description logic EL. While these problems are intractable, we propose methods for improving the reasoning times by precompiling information about the known errors and using proof-theoretic techniques for computing justifications. A prototypical implementation shows that our approach is feasible for large ontologies used in practice.

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Cassimatis, Nicholas L. Enabling More Complex and Adaptive Systems with Machine and Human Components using Automated Reasoning Methods. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada590228.

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Borgwardt, Stefan, and Rafael Peñaloza. Complementation and Inclusion of Weighted Automata on Infinite Trees: Revised Version. Technische Universität Dresden, 2011. http://dx.doi.org/10.25368/2022.180.

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Weighted automata can be seen as a natural generalization of finite state automata to more complex algebraic structures. The standard reasoning tasks for unweighted automata can also be generalized to the weighted setting. In this report we study the problems of intersection, complementation, and inclusion for weighted automata on infinite trees and show that they are not harder complexity-wise than reasoning with unweighted automata. We also present explicit methods for solving these problems optimally.

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Borgwardt, Stefan, and Rafael Peñaloza. Complementation and Inclusion of Weighted Automata on Infinite Trees. Technische Universität Dresden, 2010. http://dx.doi.org/10.25368/2022.178.

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Weighted automata can be seen as a natural generalization of finite state automata to more complex algebraic structures. The standard reasoning tasks for unweighted automata can also be generalized to the weighted setting. In this report we study the problems of intersection, complementation and inclusion for weighted automata on infinite trees and show that they are not harder than reasoning with unweighted automata. We also present explicit methods for solving these problems optimally.

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayesian deep learning. These techniques enhance AI's ability to handle uncertain, noisy, and high-dimensional data while ensuring scalability, interpretability, and trustworthiness in applications such as robotics, financial modeling, autonomous systems, and healthcare AI. Case studies demonstrate how stochastic computation improves self-driving car navigation, financial risk assessment, personalized medicine, and reinforcement learning-based automation. The findings underscore the importance of integrating probabilistic modeling with deep learning, reinforcement learning, and optimization techniques to develop AI systems that are more adaptable, scalable, and uncertainty-aware. Keywords Stochastic computation, Bayesian inference, probabilistic AI, Monte Carlo methods, Markov Chain Monte Carlo (MCMC), variational inference, uncertainty quantification, stochastic optimization, Bayesian deep learning, reinforcement learning, probabilistic graphical models, stochastic gradient descent (SGD), uncertainty-aware AI, probabilistic reasoning, risk assessment, AI in robotics, AI in finance, AI in healthcare, decision-making under uncertainty, trustworthiness in AI, scalable AI, interpretable AI.

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Badawi, Mohammed, and Mi Song Kim. Evaluating the Influence of Technology-Enhanced Teaching Methods on the Development ‎of Clinical Reasoning Skills in Medical Education: A Qualitative Systematic Review ‎. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.5.0030.

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Ma, Yue, and Rafael Peñaloza. Towards Parallel Repair Using Decompositions. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.207.

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Ontology repair remains one of the main bottlenecks for the development of ontologies for practical use. Many automated methods have been developed for suggesting potential repairs, but ultimately human intervention is required for selecting the adequate one, and the human expert might be overwhelmed by the amount of information delivered to her. We propose a decomposition of ontologies into smaller components that can be repaired in parallel. We show the utility of our approach for ontology repair, provide algorithms for computing this decomposition through standard reasoning, and study the complexity of several associated problems.

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Peñaloza, Rafael, and Anni-Yasmin Turhan. Completion-based computation of most specific concepts with limited role-depth for EL and Prob-EL⁰¹. Technische Universität Dresden, 2010. http://dx.doi.org/10.25368/2022.176.

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In Description Logics the reasoning service most specific concept (msc) constructs a concept description that generalizes an ABox individual into a concept description. For the Description Logic EL the msc may not exist, if computed with respect to general EL-TBoxes or cyclic ABoxes. However, it is still possible to find a concept description that is the msc up to a fixed role-depth, i.e. with respect to a maximal nesting of quantifiers. In this report we present a practical approach for computing the roledepth bounded msc, based on the polynomial-time completion algorithm for EL. We extend these methods to Prob-EL⁰¹c , which is a probabilistic variant of EL. Together with the companion report [9] this report devises computation methods for the bottom-up construction of knowledge bases for EL and Prob-EL⁰¹c .

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