Bibliographies thématiques / Computational reasoning

Littérature scientifique sur le sujet « Computational reasoning »

Auteur : Grafiati
Publié le 31 août 2024

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Articles de revues sur le sujet "Computational reasoning"

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Dixon, Lucas, Ross Duncan et Aleks Kissinger. « Open Graphs and Computational Reasoning ». Electronic Proceedings in Theoretical Computer Science 26 (9 juin 2010) : 169–80. http://dx.doi.org/10.4204/eptcs.26.16.

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Thompson, Errol. « Teaching Computational Reasoning Through Construals ». Education & ; Self Development 13, no 3 (30 septembre 2018) : 40–52. http://dx.doi.org/10.26907/esd13.3.05.

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Rayward-Smith, V. J., et A. Gammerman. « Computational Learning and Probabilistic Reasoning ». Journal of the Operational Research Society 48, no 7 (juillet 1997) : 756. http://dx.doi.org/10.2307/3010065.

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Tsafnat, G., et E. W. Coiera. « Computational Reasoning across Multiple Models ». Journal of the American Medical Informatics Association 16, no 6 (28 août 2009) : 768–74. http://dx.doi.org/10.1197/jamia.m3023.

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Brown, A. G. P., et F. P. Coenen. « Spatial reasoning : improving computational efficiency ». Automation in Construction 9, no 4 (juillet 2000) : 361–67. http://dx.doi.org/10.1016/s0926-5805(99)00019-9.

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Bliss, Joan, Jon Ogborn, Richard Boohan, Jonathan Briggs, Tim Brosnan, Derek Brough, Harvey Mellar et al. « Reasoning supported by computational tools ». Computers & ; Education 18, no 1-3 (janvier 1992) : 1–9. http://dx.doi.org/10.1016/0360-1315(92)90030-9.

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Guan, J. W., D. A. Bell et Z. Guan. « Computational methods for evidential reasoning ». Irish Journal of Psychology 14, no 3 (janvier 1993) : 508–9. http://dx.doi.org/10.1080/03033910.1993.10557960.

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Grass, Joshua. « Reasoning about computational resource allocation ». XRDS : Crossroads, The ACM Magazine for Students 3, no 1 (septembre 1996) : 16–20. http://dx.doi.org/10.1145/332148.332154.

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Gammerman, A. « Computational Learning and Probabilistic Reasoning ». Journal of the Operational Research Society 48, no 7 (juillet 1997) : 756–57. http://dx.doi.org/10.1057/palgrave.jors.2600381.

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Gammerman, A. « Computational Learning and Probabilistic Reasoning ». Journal of the Operational Research Society 48, no 7 (1997) : 756. http://dx.doi.org/10.1038/sj.jors.2600381.

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Thèses sur le sujet "Computational reasoning"

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Zanuttini, Bruno. « Computational Aspects of Learning, Reasoning, and Deciding ». Habilitation à diriger des recherches, Université de Caen, 2011. http://tel.archives-ouvertes.fr/tel-00995250.

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We present results and research projects about the computational aspects of classical problems in Artificial Intelligence. We are interested in the setting of agents able to describe their environment through a possibly huge number of Boolean descriptors, and to act upon this environment. The typical applications of this kind of studies are to the design of autonomous robots (for exploring unknown zones, for instance) or of software assistants (for scheduling, for instance). The ultimate goal of research in this domain is the design of agents able to learn autonomously, by learning and interacting with their environment (including human users), also able to reason for producing new pieces of knowledge, for explaining observed phenomena, and finally, able to decide on which action to take at any moment, in a rational fashion. Ideally, such agents will be fast, efficient as soon as they start to interact with their environment, they will improve their behavior as time goes by, and they will be able to communicate naturally with humans. Among the numerous research questions raised by these objectives, we are especially interested in concept and preference learning, in reinforcement learning, in planning, and in some underlying problems in complexity theory. A particular attention is paid to interaction with humans and to huge numbers of descriptors of the environment, as are necessary in real-world applications.
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Pease, Alison. « A computational model of Lakatos-style reasoning ». Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2113.

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Lakatos outlined a theory of mathematical discovery and justification, which suggests ways in which concepts, conjectures and proofs gradually evolve via interaction between mathematicians. Different mathematicians may have different interpretations of a conjecture, examples or counterexamples of it, and beliefs regarding its value or theoremhood. Through discussion, concepts are refined and conjectures and proofs modified. We hypothesise that: (i) it is possible to computationally represent Lakatos's theory, and (ii) it is useful to do so. In order to test our hypotheses we have developed a computational model of his theory. Our model is a multiagent dialogue system. Each agent has a copy of a pre-existing theory formation system, which can form concepts and make conjectures which empirically hold for the objects of interest supplied. Distributing the objects of interest between agents means that they form different theories, which they communicate to each other. Agents then find counterexamples and use methods identified by Lakatos to suggest modifications to conjectures, concept definitions and proofs. Our main aim is to provide a computational reading of Lakatos's theory, by interpreting it as a series of algorithms and implementing these algorithms as a computer program. This is the first systematic automated realisation of Lakatos's theory. We contribute to the computational philosophy of science by interpreting, clarifying and extending his theory. We also contribute by evaluating his theory, using our model to test hypotheses about it, and evaluating our extended computational theory on the basis of criteria proposed by several theorists. A further contribution is to automated theory formation and automated theorem proving. The process of refining conjectures, proofs and concept definitions requires a flexibility which is inherently useful in fields which handle ill-specified problems, such as theory formation. Similarly, the ability to automatically modify an open conjecture into one which can be proved, is a valuable contribution to automated theorem proving.
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Sanchez, Roberto. « Improving Computational Efficiency in Context-Based Reasoning Simulations ». Honors in the Major Thesis, University of Central Florida, 2003. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/416.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf
Bachelors
Engineering and Computer Science
Computer Engineering
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Broxvall, Mathias. « A Study in the Computational Complexity of Temporal Reasoning ». Doctoral thesis, Linköping : Univ, 2002. http://www.ep.liu.se/diss/science_technology/07/79/index.html.

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Griffith, Todd W. « A computational theory of generative modeling in scientific reasoning ». Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/8177.

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Wong, Yiu Kwong. « Application of computational models and qualitative reasoning to economics ». Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/688.

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Schoter, Andreas. « The computational application of bilattice logic to natural reasoning ». Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/434.

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Chapter 1 looks at natural reasoning. It begins by considering the inferences that people make, particularly in terms of how those inferences differ from what is sanctioned by classical logic. I then consider the role of logic in relation to psychology and compare this relationship with the competence/performance distinction from syntax. I discuss four properties of natural reasoning that I believe are key to any theory: specifically partially, paraconsistancy, relevance and defeasibility. I then discuss whether these are semantic properties or pragmatic ones, and conclude by describing a new view of logic and inference prevalent in some contemporary writings. Chapter 2 looks at some of the existing formal approaches to the four properties. For each property I present the basic idea in formal terms, and then discuss a number of systems from the literature. Each section concludes with a brief discussion of the importance of the given property in the field of computation. Chapter 3 develops the formal system used in this thesis. this is an evidential, bilattice-based logic (EBL). I begin by presenting the mathematical preliminaries, and then show how the four properties of natural reasoning can be captured. The details of the logic itself are presented, beginning with the syntax and then moving on to the semantics. The role of pragmatic inferences in the logic is considered and a formal solution is advanced. I conclude by comparing EBL to some of the logics discussed in Chapter 2. Chapter 4 rounds off Part 1 by considering the implementation of the logic and some of it's computational properties. It begins by considering the application of evidential bilattice logic to logic programming; it extends Fitting's work in this area to construct a programming language, QLOG2. I give some examples of this language in use. The QLOG2 language is then used as a part of the implementation of the EBL system itself: I describe the details of this Implementation and then give some examples of the system in use. The chapter concludes by giving an informal presentation of some basic complexity results for logical closure in EBL, based on the given implementation. Chapter 5 presents some interesting data from linguistics that reflects some of the principles of natural reasoning; in particular I concentrate on implicatures and presupposition. I begin by describing the data and then consider a number of approaches from both the logical and the linguistic literature. Chapter 6 uses the logic developed in Chapter 3 to analyse the data presented in Chapter 5. I consider the basic inference cases, and then move on to more complex examples involving contextual interactions. The results are quite successful, and add weight to Mercer's quest for a common logical semantics for entailment and presupposition. All of the examples considered in this chapter can be handled by the implemented system described in Chapter 4. Finally, Chapter 7 rounds off by presenting some further areas of research that have been raised by this investigation. In particular, the issues of quantification and modality are discussed.
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Hatzilygeroudis, Ioannis. « Integrating logic and objects for knowledge representation and reasoning ». Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334808.

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Aronoff, Caroline Bradley. « A computational characterization of domain-based causal reasoning development in children ». Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119744.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-72).
To better understand human intelligence, we must first understand how humans use and learn from stories. One important aspect of how humans learn from stories is our ability to reason about cause and eect. Psychological evidence suggests that when children develop the ability to learn cause-and-eect relationships from stories, they do so in discrete stages where each new stage enables the child to incorporate new kinds of information. In this thesis, I attempt to shed light on the mechanisms that underlie the development of causal reasoning in children. I create a behavior-level model, an explanatory theory, and an explanation-level model that account for the developmental stages. I implement these models on top of the Genesis Story Understanding System. The result is a psychologically plausible explanation-level model that captures the observed causal reasoning behaviors of children at dierent stages of developments. The model also takes the observations from psychological evidence to another level by proposing mechanisms that enable such development in children.
by Caroline Bradley Aronoff.
M. Eng.
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Fischer, Olivier. « Cognitively plausible heuristics to tackle the computational complexity of abductive reasoning / ». The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487694389394369.

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Livres sur le sujet "Computational reasoning"

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A, Gammerman, dir. Computational learning and probabilistic reasoning. Chichester : Wiley, 1996.

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1942-, Chandrasekaran B., Glasgow Janice et Narayanan N. Hari, dir. Diagrammatic reasoning : Cognitive and computational perspectives. Menlo Park, Calif : AAAI Press, 1995.

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Prade, Henri, et Gilles Richard, dir. Computational Approaches to Analogical Reasoning : Current Trends. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54516-0.

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1971-, Feeney Aidan, et Heit Evan 1965-, dir. Inductive reasoning : Experimental, developmental, and computational approaches. Cambridge : Cambridge University Press, 2007.

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Magnani, Lorenzo, Nancy J. Nersessian et Claudio Pizzi, dir. Logical and Computational Aspects of Model-Based Reasoning. Dordrecht : Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0550-0.

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Lorenzo, Magnani, Nersessian Nancy J, Pizzi Claudio 1944- et International Conference on Model-Based Reasoning : Scientific Discovery, Technological Innovation, Values (2001 : Pavia, Italy )., dir. Logical and computational aspects of model-based reasoning. Dordrecht : Kluwer Academic, 2002.

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Vapnik, Vladimir Naumovich. The nature of statistical learning theory. New York : Springer, 1995.

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Rennels, Glenn D. A Computational Model of Reasoning from the Clinical Literature. Berlin, Heidelberg : Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-93363-9.

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Rennels, Glenn D. A Computational model of reasoning from the clinical literature. Berlin : Springer-Verlag, 1987.

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Italy) COMMA (Conference) (3rd 2010 Desenzano del Garda. Computational models of argument : Proceedings of COMMA 2010. Amsterdam : IOS Press, 2010.

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Chapitres de livres sur le sujet "Computational reasoning"

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Hausser, Roland. « Logical Reasoning ». Dans Computational Cognition, 51–67. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37499-9_4.

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Hausser, Roland. « Common Sense Reasoning ». Dans Computational Cognition, 69–79. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37499-9_5.

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Dalla Chiara, M., R. Giuntini et R. Greechie. « Quantum computational logic ». Dans Reasoning in Quantum Theory, 249–66. Dordrecht : Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0526-4_17.

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Rosati, Riccardo. « Integrating Ontologies and Rules : Semantic and Computational Issues ». Dans Reasoning Web, 128–51. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11837787_5.

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Hendrickson, Noel. « Applied Counterfactual Reasoning ». Dans Computational Methods for Counterterrorism, 249–62. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01141-2_13.

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Martin, Ursula. « Computers, Reasoning and Mathematical Practice ». Dans Computational Logic, 301–46. Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58622-4_9.

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Backhouse, Roland. « Datatype-Generic Reasoning ». Dans Logical Approaches to Computational Barriers, 21–34. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11780342_3.

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Cornelis, Chris, et Etienne E. Kerre. « Inclusion-Based Approximate Reasoning ». Dans Computational Science - ICCS 2001, 221–30. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45718-6_25.

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Polkowski, Lech T. « Approximate Reasoning : Rough Logics ». Dans Studies in Computational Intelligence, 207–34. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91680-0_7.

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Dũng, Phan Minh, et Tran Cao Son. « Default Reasoning with Specificity ». Dans Computational Logic — CL 2000, 792–806. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44957-4_53.

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Actes de conférences sur le sujet "Computational reasoning"

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Dias, Rafael, Aniko Costa, Jose Malaquias et Manuel Camara. « Teaching Computational Reasoning without a Computer ». Dans IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2019. http://dx.doi.org/10.1109/iecon.2019.8927169.

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Hogan, Alexander H., Kevin Hogan et Christian Tilt. « On the Cruelty of Computational Reasoning ». Dans Politics of the Machines - Art and After. BCS Learning & Development, 2018. http://dx.doi.org/10.14236/ewic/evac18.3.

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Walker, Vern R., Ji Hae Han, Xiang Ni et Kaneyasu Yoseda. « Semantic types for computational legal reasoning ». Dans ICAIL '17 : Sixteenth International Conference on Artificial Intelligence and Law. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3086512.3086535.

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Nitta, Katsumi, Stephen Wong et Yoshihisa Ohtake. « A computational model for trial reasoning ». Dans the fourth international conference. New York, New York, USA : ACM Press, 1993. http://dx.doi.org/10.1145/158976.158979.

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St-Vincent, Pierre, Daniel Poulin et Paul Bratley. « A computational framework for dialectical reasoning ». Dans the fifth international conference. New York, New York, USA : ACM Press, 1995. http://dx.doi.org/10.1145/222092.222224.

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Maurer, Peter M. « Teaching Induction and Deductive Reasoning ». Dans 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00368.

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Wallner, Johannes P. « Computational Argumentation : Reasoning, Dynamics, and Supporting Explainability ». Dans Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California : International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/986.

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This overview accompanies the author's Early Career Track presentation. We survey recent research and research agenda of the author, focusing on contributions in the area of computational argumentation. Contributions span from foundations of static and dynamic forms of argumentative reasoning and approaches to support explainability, e.g., analysis of the computational complexity of argumentative reasoning and algorithmic approaches.
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Mirzaee, Roshanak, et Parisa Kordjamshidi. « Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning ». Dans Findings of the Association for Computational Linguistics : EMNLP 2023. Stroudsburg, PA, USA : Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-emnlp.221.

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Canavotto, Ilaria, et John Horty. « Piecemeal Knowledge Acquisition for Computational Normative Reasoning ». Dans AIES '22 : AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3514094.3534182.

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Ge, Qiang, et Fengbin Zheng. « Study on Family Relations Reasoning Based on Automated Reasoning ». Dans 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677058.

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Rapports d'organisations sur le sujet "Computational reasoning"

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SENGLAUB, MICHAEL E., DAVID L. HARRIS et ELAINE M. RAYBOURN. Foundations for Reasoning in Cognition-Based Computational Representations of Human Decision Making. Office of Scientific and Technical Information (OSTI), novembre 2001. http://dx.doi.org/10.2172/789585.

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Borgwardt, Stefan, Felix Distel et Rafael Peñaloza. Gödel Description Logics : Decidability in the Absence of the Finitely-Valued Model Property. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.199.

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In the last few years there has been a large effort for analysing the computational properties of reasoning in fuzzy Description Logics. This has led to a number of papers studying the complexity of these logics, depending on their chosen semantics. Surprisingly, despite being arguably the simplest form of fuzzy semantics, not much is known about the complexity of reasoning in fuzzy DLs w.r.t. witnessed models over the Gödel t-norm. We show that in the logic G-IALC, reasoning cannot be restricted to finitely valued models in general. Despite this negative result, we also show that all the standard reasoning problems can be solved in this logic in exponential time, matching the complexity of reasoning in classical ALC.
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Borgwardt, Stefan, Marcel Lippmann et Veronika Thost. Reasoning with Temporal Properties over Axioms of DL-Lite. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.208.

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Recently, a lot of research has combined description logics (DLs) of the DL-Lite family with temporal formalisms. Such logics are proposed to be used for situation recognition and temporalized ontology-based data access. In this report, we consider DL-Lite-LTL, in which axioms formulated in a member of the DL-Lite family are combined using the operators of propositional linear-time temporal logic (LTL). We consider the satisfiability problem of this logic in the presence of so-called rigid symbols whose interpretation does not change over time. In contrast to more expressive temporalized DLs, the computational complexity of this problem is the same as for LTL, even w.r.t. rigid symbols.
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Bonatti, Piero, Carsten Lutz et Frank Wolter. Expressive Non-Monotonic Description Logics Based on Circumscription. Technische Universität Dresden, 2005. http://dx.doi.org/10.25368/2022.149.

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Recent applications of description logics (DLs) strongly suggest the integration of non-monotonic features into DLs, with particular attention to defeasible inheritance. However, the existing non-monotonic extensions of DLs are usually based on default logic or autoepistemic logic, and have to be seriously restricted in expressive power to preserve the decidability of reasoning. In particular, such DLs allow the modelling of defeasible inheritance only in a very restricted form, where non-monotonic reasoning is limited to individuals that are explicitly identified by constants in the knowledge base. In this paper, we consider non-monotonic extensions of expressive DLs based on circumscription. We prove that reasoning in such DLs is decidable even without the usual, strong restrictions in expressive power. We pinpoint the exact computational complexity of reasoning as complete for NPNEXP and NEXPNP, depending on whether or not the number of minimized and fixed predicates is assumed to be bounded by a constant. These results assume that only concept names (and no role names) can be minimized and fixed during minimization. On the other hand, we show that fixing role names during minimization makes reasoning undecidable.
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Distel, Felix. Model-based Most Specific Concepts in Description Logics with Value Restrictions. Technische Universität Dresden, 2008. http://dx.doi.org/10.25368/2022.167.

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Non-standard inferences are particularly useful in the bottom-up construction of ontologies in description logics. One of the more common non-standard reasoning tasks is the most specific concept (msc) for an ABox-individual. In this paper we present similar non-standard reasoning task: most specific concepts for models (model-mscs). We show that, although they look similar to ABox-mscs their computational behaviour can be different. We present constructions for model-mscs in FL₀ and FLE with cyclic TBoxes and for ALC∪∗ with acyclic TBoxes. Since subsumption in FLE with cyclic TBoxes has not been examined previously, we present a characterization of subsumption and give a construction for the least common subsumer in this setting.
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Lutz, Carsten, et Maja Miličić. Description Logics with Concrete Domains and Functional Dependencies. Technische Universität Dresden, 2004. http://dx.doi.org/10.25368/2022.143.

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Description Logics (DLs) with concrete domains are a useful tool in many applications. To further enhance the expressive power of such DLs, it has been proposed to add database-style key constraints. Up to now, however, only uniqueness constraints have been considered in this context, thus neglecting the second fundamental family of key constraints: functional dependencies. In this paper, we consider the basic DL with concrete domains ALC(D), extend it with functional dependencies, and analyze the impact of this extension on the decidability and complexity of reasoning. Though intuitively the expressivity of functional dependencies seems weaker than that of uniqueness constraints, we are able to show that the former have a similarly severe impact on the computational properties: reasoning is undecidable in the general case, and NExpTime-complete in some slightly restricted variants of our logic.
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Liu, Hongkai, Carsten Lutz, Maja Miličić et Frank Wolter. Description Logic Actions with general TBoxes : a Pragmatic Approach. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.156.

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Action formalisms based on description logics (DLs) have recently been introduced as decidable fragments of well-established action theories such as the Situation Calculus and the Fluent Calculus. However, existing DL action formalisms fail to include general TBoxes, which are the standard tool for formalising ontologies in modern description logics. We define a DL action formalism that admits general TBoxes, propose an approach to addressing the ramification problem that is introduced in this way, and perform a detailed investigation of the decidability and computational complexity of reasoning in our formalism.
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Lutz, Carsten, et Frank Wolter. Modal Logics of Topological Relations. Technische Universität Dresden, 2004. http://dx.doi.org/10.25368/2022.142.

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The eight topological RCC8(or Egenhofer-Franzosa)- relations between spatial regions play a fundamental role in spatial reasoning, spatial and constraint databases, and geographical information systems. In analogy with Halpern and Shoham’s modal logic of time intervals based on the Allen relations, we introduce a family of modal logics equipped with eight modal operators that are interpreted by the RCC8-relations. The semantics is based on region spaces induced by standard topological spaces, in particular the real plane. We investigate the expressive power and computational complexity of the logics obtained in this way. It turns our that, similar to Halpern and Shoham’s logic, the expressive power is rather natural, but the computational behavior is problematic: topological modal logics are usually undecidable and often not even recursively enumerable. This even holds if we restrict ourselves to classes of finite region spaces or to substructures of region spaces induced by topological spaces. We also analyze modal logics based on the set of RCC5relations, with similar results.
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Gil, Oliver Fernández, et Anni-Yasmin Turhan. Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics. Technische Universität Dresden, 2020. http://dx.doi.org/10.25368/2022.261.

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Classical regular path queries (RPQs) can be too restrictive for some applications and answering such queries under approximate semantics to relax the query is desirable. While for answering regular path queries over graph databases under approximate semantics algorithms are available, such algorithms are scarce for the ontology-mediated setting. In this paper we extend an approach for answering RPQs over graph databases that uses weighted transducers to approximate paths from the query in two ways. The first extension is to answering approximate conjunctive 2-way regular path queries (C2RPQs) over graph databases and the second is to answering C2RPQs over ELH and DL-LiteR ontologies. We provide results on the computational complexity of the underlying reasoning problems and devise approximate query answering algorithms.
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Lutz, Carsten, Carlos Areces, Ian Horrocks et Ulrike Sattler. Keys, Nominals, and Concrete Domains. Technische Universität Dresden, 2002. http://dx.doi.org/10.25368/2022.122.

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Many description logics (DLs) combine knowledge representation on an abstract, logical level with an interface to 'concrete' domains such as numbers and strings with built-in predicates such as <, +, and prefix-of. These hybrid DLs have turned out to be quite useful for reasoning about conceptual models of information systems, and as the basis for expressive ontology languages. We propose to further extend such DLs with key constraints that allow the expression of statements like 'US citizens are uniquely identified by their social security number'. Based on this idea, we introduce a number of natural description logics and perform a detailed analysis of their decidability and computational complexity. It turns out that naive extensions with key constraints easily lead to undecidability, whereas more careful extensions yield NEXPTIME-complete DLs for a variety of useful concrete domains.
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