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Relevant bibliographies by topics / Automatic reasoning

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Academic literature on the topic 'Automatic reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Automatic reasoning"

1

Apperly, Ian A., Kevin J. Riggs, Andrew Simpson, Claudia Chiavarino, and Dana Samson. "Is Belief Reasoning Automatic?" Psychological Science 17, no. 10 (October 2006): 841–44. http://dx.doi.org/10.1111/j.1467-9280.2006.01791.x.

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邱, 德钧. "Automatic Logic Reasoning in Artificial Intelligence." Artificial Intelligence and Robotics Research 08, no. 01 (2019): 7–16. http://dx.doi.org/10.12677/airr.2019.81002.

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Ferles, Kostas, Jacob Van Geffen, Isil Dillig, and Yannis Smaragdakis. "Symbolic Reasoning for Automatic Signal Placement." ACM SIGOPS Operating Systems Review 54, no. 1 (August 31, 2020): 64–76. http://dx.doi.org/10.1145/3421473.3421482.

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Arendasy, Martin, Markus Sommer, Georg Gittler, and Andreas Hergovich. "Automatic Generation of Quantitative Reasoning Items." Journal of Individual Differences 27, no. 1 (January 2006): 2–14. http://dx.doi.org/10.1027/1614-0001.27.1.2.

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This paper deals with three studies on the computer-based, automatic generation of algebra word problems. The cognitive psychology based generative/quality control frameworks of the item generator are presented. In Study I the quality control framework is empirically tested using a first set of automatically generated items. Study II replicates the findings of Study I using a larger set of automatically generated algebra word problems. Study III deals with the generative framework of the item generator by testing construct validity aspects of the item generator produced items. Using nine Rasch-homogeneous subscales of the new intelligence structure battery (INSBAT, Hornke et al., 2004 ), a hierarchical confirmatory factor analysis is reported, which provides first evidence of convergent as well as divergent validity of the automatically generated items. The end of the paper discusses possible advantages of automatic item generation in general ranging from test security issues and the possibility of a more precise psychological assessment to mass testing and economical questions of test construction.

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SHETH, AMIT P., SUNIT K. GALA, and SHAMKANT B. NAVATHE. "ON AUTOMATIC REASONING FOR SCHEMA INTEGRATION." International Journal of Cooperative Information Systems 02, no. 01 (March 1993): 23–50. http://dx.doi.org/10.1142/s0218215793000034.

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Success in database schema integration depends on the ability to capture real world semantics of the schema objects, and to reason about the semantics. Earlier schema integration approaches mainly rely on heuristics and human reasoning. In this paper, we discuss an approach to automate a significant part of the schema integration process. Our approach consists of three phases. An attribute hierarchy is generated in the first phase. This involves identifying relationships (equality, disjointness and inclusion) among attributes. We discuss a strategy based on user-specified semantic clustering. In the second phase, a classification algorithm based on the semantics of class subsumption is applied to the class definitions and the attribute hierarchy to automatically generate a class taxonomy. This class taxonomy represents a partially integrated schema. In the third phase, the user may employ a set of well-defined comparison operators in conjunction with a set of restructuring operators, to further modify the schema. These operators as well as the automatic reasoning during the second phase are based on subsumption. The formal semantics and automatic reasoning utilized in the second phase is based on a terminological logic as adapted in the CANDIDE data model. Classes are completely defined in terms of attributes and constraints. Our observation is that the inability to completely define attributes and thus completely capture their real world semantics imposes a fundamental limitation on the possibility of automatically reasoning about attribute definitions. This necessitates human reasoning during the first phase of the integration approach.

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Ferles, Kostas, Jacob Van Geffen, Isil Dillig, and Yannis Smaragdakis. "Symbolic reasoning for automatic signal placement." ACM SIGPLAN Notices 53, no. 4 (December 2, 2018): 120–34. http://dx.doi.org/10.1145/3296979.3192395.

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Oswald, Margit E., and Ingrid Stucki. "Automatic Judgment and Reasoning About Punishment." Social Justice Research 23, no. 4 (December 2010): 290–307. http://dx.doi.org/10.1007/s11211-010-0120-5.

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Shpitalni, M., and H. Lipson. "Automatic Reasoning for Design under Geometrical Constraints." CIRP Annals 46, no. 1 (1997): 85–88. http://dx.doi.org/10.1016/s0007-8506(07)60781-1.

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Abánades, Miguel, Francisco Botana, Zoltán Kovács, Tomás Recio, and Csilla Sólyom-Gecse. "Development of automatic reasoning tools in GeoGebra." ACM Communications in Computer Algebra 50, no. 3 (November 4, 2016): 85–88. http://dx.doi.org/10.1145/3015306.3015309.

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Shen, Tzung-Sz, Jianbing Huang, and Chia-Hsiang Menq. "Multiple-Sensor Planning and Information Integration for Automatic Coordinate Metrology." Journal of Computing and Information Science in Engineering 1, no. 2 (May 1, 2001): 167–79. http://dx.doi.org/10.1115/1.1385827.

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Multiple-sensor integration of vision and touch probe sensors has been shown to be a feasible approach for rapid and high-precision coordinate acquisition [Shen, T. S., Huang, J., and Meng, C. H., 2000, “Multiple-sensor integration for rapid and high-precision coordinate metrology,” IEEE/ASME Trans. Mechatron. 5, pp. 110–121]. However, the automation of coordinate measurements is still hindered by unknown surface areas that cannot be digitized using the vision system due to occlusions. It is identified that the estimation and reasoning of unknown surface areas, and automatic sensor planning using multiple sensors are two key issues. In order to advance multiple-sensor integration technologies toward a fully automatic and agile coordinate metrology, information integration algorithms for estimating and reasoning unknown surface areas, and an automatic multiple-sensor planning environment are developed in this paper. Experimental and simulation results are also demonstrated.

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Dissertations / Theses on the topic "Automatic reasoning"

1

Robertson, Neil. "Automatic causal reasoning for video surveillance." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432567.

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Urbas, Matej. "Mechanising heterogeneous reasoning in theorem provers." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708290.

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Khoshnevisan-Tehrani, Hassam. "Automatic transformation systems based on function-level reasoning." Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/46937.

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Eliassen, Lars Moland. "Automatic Fish Classification : Using Image Processing and Case-Based Reasoning." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18457.

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Counting and classifying fish moving upstream in rivers to spawn is a useful way of monitoring the population of different species. Today, there exist some commercial solutions, along with some research that addresses the area. Case-based reasoning is a process that can be used to solve new problems based on previous problems. This thesis studies the possibilities of combining image processing techniques and case-based reasoning to classify species of fish which are similar to each other in both shape, size and color. Methods for image preprocessing are discussed, and tested. Methods for feature extraction and a case-based reasoning prototype are proposed, implemented and tested with promising results.

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Woodbury, Charla Jean. "Automatic Extraction From and Reasoning About Genealogical Records: A Prototype." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2335.

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Family history research on the web is increasing in popularity, and many competing genealogical websites host large amounts of data-rich, unstructured, primary genealogical records. It is labor-intensive, however, even after making these records machine-readable, for humans to make these records easily searchable. What we need are computer tools that can automatically produce indices and databases from these genealogical records and can automatically identify individuals and events, determine relationships, and put families together. We propose here a possible solution—specialized ontologies, built specifically for extracting information from primary genealogical records, with expert logic and rules to infer genealogical facts and assemble relationship links between persons with respect to the genealogical events in their lives. The deliverables of this solution are extraction ontologies that can extract from parish or town records, annotated versions of original documents, data files of individuals and events, and rules to infer family relationships from stored data. The solution also provides for the ability to query over the rules and data files and to obtain query-result justification linking back to primary genealogical records. An evaluation of the prototype solution shows that the extraction has excellent recall and precision results and that inferred facts are correct.

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Low, Harold William Capen IV. "Story understanding in Genesis : exploring automatic plot construction through commonsense reasoning." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66440.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 72).
Whether through anecdotes, folklore, or formal history, humans learn the lessons and expectations of life from stories. If we are to build intelligent programs that learn as humans do, such programs must understand stories as well. Casting narrative text in an information-rich representation affords Al research platforms, such as the Genesis system, the capacity to understand the events of stories individually. To understand a story, however, a program must understand not just events, but also how events cause and motivate one another. In order to understand the relationships between these events, stories must be saturated with implicit details, connecting given events into coherent plot arcs. In my research, my first step was to analyze a range of story summaries in detail. Using nearly 50 rules, applicable to brief summaries of stories taken from international politics, group dynamics, and basic human emotion, I demonstrate how a rendition of Frank Herbert's Dune can be automatically understood so as to produce an interconnected story network of over one hundred events. My second step was to explore the nuances of rule construction, finding which rules are needed to create story networks reflective of proper implicit understanding and how we, as architects, must shape those rules to be understood. In particular, I develop a method that constructs new rules using the rules already embedded in stories, a representation of higher-order thinking that enables us to speak of our ideas as objects.
by Harold William Capen Low, IV.
M.Eng.

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Rode, Benjamin Paul. "Making sense of common sense : learning, fallibilism, and automated reasoning /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004366.

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Moshir, Moghaddam Kianosh. "Automated Reasoning Support for Invasive Interactive Parallelization." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84830.

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To parallelize a sequential source code, a parallelization strategy must be defined that transforms the sequential source code into an equivalent parallel version. Since parallelizing compilers can sometimes transform sequential loops and other well-structured codes into parallel ones automatically, we are interested in finding a solution to parallelize semi-automatically codes that compilers are not able to parallelize automatically, mostly because of weakness of classical data and control dependence analysis, in order to simplify the process of transforming the codes for programmers.Invasive Interactive Parallelization (IIP) hypothesizes that by using anintelligent system that guides the user through an interactive process one can boost parallelization in the above direction. The intelligent system's guidance relies on a classical code analysis and pre-defined parallelizing transformation sequences. To support its main hypothesis, IIP suggests to encode parallelizing transformation sequences in terms of IIP parallelization strategies that dictate default ways to parallelize various code patterns by using facts which have been obtained both from classical source code analysis and directly from the user.In this project, we investigate how automated reasoning can supportthe IIP method in order to parallelize a sequential code with an acceptable performance but faster than manual parallelization. We have looked at two special problem areas: Divide and conquer algorithms and loops in the source codes. Our focus is on parallelizing four sequential legacy C programs such as: Quick sort, Merge sort, Jacobi method and Matrix multipliation and summation for both OpenMP and MPI environment by developing an interactive parallelizing assistance tool that provides users with the assistanceneeded for parallelizing a sequential source code.

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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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Fuchs, Alexander Tinelli C. "Evolving model evolution." [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/361.

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Books on the topic "Automatic reasoning"

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Analogy of automated reasoning. Boston: Academic Press, 1990.

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Kalman, John Arnold. Automated reasoning with Otter. Princeton, N.J: Rinton Press, 2001.

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Automated reasoning: 33 basic research problems. Englewood Cliffs, N.J: Prentice-Hall, 1988.

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Handbook of practical logic and automated reasoning. Cambridge: Cambridge University Press, 2009.

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IFIP TC 12/WG 12.3 International Workshop on Automated Reasoning (1992 Beijing, China). Automated reasoning: Proceedings of the IFIP TC12/WG12.3 International Workshop on Automated Reasoning, Beijing, P.R. China, 13-16 July 1992. Amsterdam: North-Holland, 1992.

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Schaub, Torsten. The automation of reasoning with incomplete information: From semantic foundations to efficient computation. Berlin: Springer, 1997.

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The automation of reasoning with incomplete information: From semantic foundations to efficient computation. Berlin: Springer, 1997.

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1945-, Gabbay Dov M., and Ohlbach Hans Jürgen, eds. Practical reasoning: International Conference on Formal and Applied Practical Reasoning, FAPR '96, Bonn, Germany, June 1996 : proceedings. Berlin: Springer, 1996.

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1956-, Kerber Manfred, and Kohlhase Michael 1964-, eds. Symbolic computation and automated reasoning: The CALCULEMUS-2000 Symposium. Natick, Mass: A K Peters, 2001.

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Zhongzhi, Shi, ed. Automated reasoning: Proceedings of the IFIP TC 12/WG 12.3 International Workshop on Automated Reasoning, Beijing, P.R. China, 13-16 July 1992. Amsterdam: North-Holland, 1992.

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Book chapters on the topic "Automatic reasoning"

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Jean-Baptiste, Lamy. "Automatic reasoning." In Ontologies with Python, 157–85. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6552-9_7.

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Brown, Chad E. "Satallax: An Automatic Higher-Order Prover." In Automated Reasoning, 111–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31365-3_11.

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Sorge, Volker, Andreas Meier, Roy McCasland, and Simon Colton. "Automatic Construction and Verification of Isotopy Invariants." In Automated Reasoning, 36–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11814771_5.

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Colton, Simon, Andreas Meier, Volker Sorge, and Roy McCasland. "Automatic Generation of Classification Theorems for Finite Algebras." In Automated Reasoning, 400–414. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25984-8_30.

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Niemelä, Ilkka. "Towards automatic autoepistemic reasoning." In Lecture Notes in Computer Science, 428–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/bfb0018457.

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Stark, W. Richard. "Automatic Reasoning, Algebraic Intelligence." In LISP, Lore, and Logic, 216–39. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4613-8931-6_8.

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Giesl, Jürgen, Peter Schneider-Kamp, and René Thiemann. "AProVE 1.2: Automatic Termination Proofs in the Dependency Pair Framework." In Automated Reasoning, 281–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11814771_24.

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Benzmüller, Christoph, Lawrence C. Paulson, Frank Theiss, and Arnaud Fietzke. "LEO-II - A Cooperative Automatic Theorem Prover for Classical Higher-Order Logic (System Description)." In Automated Reasoning, 162–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-71070-7_14.

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Stratulat, Sorin. "Automatic ‘Descente Infinie’ Induction Reasoning." In Lecture Notes in Computer Science, 262–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554554_20.

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Ballantyne, A. M. "The Metatheorist: Automatic Proofs of Theorems in Analysis Using Non-Standard Techniques, Part II." In Automated Reasoning Series, 61–75. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3488-0_3.

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Conference papers on the topic "Automatic reasoning"

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Ireland, Andrew. "Cooperative reasoning for automatic software verification." In the second workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1345169.1345175.

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Jin Li and Ping He. "Extended automatic reasoning of criminal investigation." In 2009 International Conference on Industrial Mechatronics and Automation (ICIMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/icima.2009.5156616.

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Ferles, Kostas, Jacob Van Geffen, Isil Dillig, and Yannis Smaragdakis. "Symbolic reasoning for automatic signal placement." In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192366.3192395.

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Gu, Minqiang, and Qiang Liu. "Automatic compositional reasoning for multi-thread programs." In 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2011. http://dx.doi.org/10.1109/cscwd.2011.5960072.

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FISCHER, Thomas, Alexander ARTSCHWAGER, Konrad PFLEIDERER, Anke RISSIEK, Magdalena MANDALKA, Andreas SEIDL, and Rainer TRIEB. "Automatic Morphological Classification with Case-Based Reasoning." In 7th International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 30 Nov.-1 Dec. 2016. Ascona, Switzerland: Hometrica Consulting - Dr. Nicola D'Apuzzo, 2016. http://dx.doi.org/10.15221/16.148.

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Char, B. W. "Automatic reasoning about numerical stability of rational expressions." In the ACM-SIGSAM 1989 international symposium. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74540.74569.

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Basile, Cataldo, Daniele Canavese, Jerome D'Annoville, Bjorn De Sutter, and Fulvio Valenza. "Automatic Discovery of Software Attacks via Backward Reasoning." In 2015 IEEE/ACM 1st International Workshop on Software Protection (SPRO). IEEE, 2015. http://dx.doi.org/10.1109/spro.2015.17.

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Lele, Adway, Jayant Kirtane, and Ambuja Salgaonkar. "Siddhataa: Automatic theorem prover based on equational reasoning." In 2011 World Congress on Information and Communication Technologies (WICT). IEEE, 2011. http://dx.doi.org/10.1109/wict.2011.6141445.

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Wei, Bo, Zhi Jin, and Didar Zowghi. "An Automatic Reasoning Mechanism for NFR Goal Models." In 2011 IEEE 5th International Symposium on Theoretical Aspects of Software Engineering (TASE). IEEE, 2011. http://dx.doi.org/10.1109/tase.2011.13.

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Jinhai, Zhang. "Study and implementation of automatic fingerprint recognition technology." In 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE). IEEE, 2011. http://dx.doi.org/10.1109/urke.2011.6007902.

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Reports on the topic "Automatic reasoning"

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Mulligan, Casey. Automated Economic Reasoning with Quantifier Elimination. Cambridge, MA: National Bureau of Economic Research, December 2016. http://dx.doi.org/10.3386/w22922.

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Lidsky, L., and A. Dobrzeniecki. Technical specifications requirements: Automated reasoning applications. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/7172273.

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Mulligan, Casey, Russell Bradford, James Davenport, Matthew England, and Zak Tonks. Non-linear Real Arithmetic Benchmarks derived from Automated Reasoning in Economics. Cambridge, MA: National Bureau of Economic Research, May 2018. http://dx.doi.org/10.3386/w24602.

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Erbacher, Robert F., and Steve E. Hutchinson. Extending Case-Based Reasoning (CBR) Approaches to Semi-automated Network Alert Reporting. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada584553.

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Wos, L. Theory Institute in Automated Reasoning held at Argonne National Laboratory, August 6--10, 1990. Office of Scientific and Technical Information (OSTI), August 1990. http://dx.doi.org/10.2172/6540934.

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Xavier, P. G., and R. A. LaFarge. A configuration space toolkit for automated spatial reasoning: Technical results and LDRD project final report. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/453746.

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

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Burd, W., D. Culler, T. Eskridge, L. Cox, and T. Slater. The Milling Assistant, Case-Based Reasoning, and machining strategy: A report on the development of automated numerical control programming systems at New Mexico State University. Office of Scientific and Technical Information (OSTI), August 1993. http://dx.doi.org/10.2172/10184924.

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