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Relevant bibliographies by topics / Temporal reasoning

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Academic literature on the topic 'Temporal reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Temporal reasoning"

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Augusto, Juan C., and Guillermo R. Simari. "Temporal Defeasible Reasoning." Knowledge and Information Systems 3, no. 3 (August 2001): 287–318. http://dx.doi.org/10.1007/pl00011670.

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Emerson, E. A., A. K. Mok, A. P. Sistla, and J. Srinivasan. "Quantitative temporal reasoning." Real-Time Systems 4, no. 4 (December 1992): 331–52. http://dx.doi.org/10.1007/bf00355298.

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Keravnou, Elpida. "Medical temporal reasoning." Artificial Intelligence in Medicine 3, no. 6 (December 1991): 289–90. http://dx.doi.org/10.1016/0933-3657(91)90001-r.

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Chen, Xiaojun, Shengbin Jia, Ling Ding, and Yang Xiang. "Reasoning over temporal knowledge graph with temporal consistency constraints." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11941–50. http://dx.doi.org/10.3233/jifs-210064.

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Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP, which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10.

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Ringel, Felix. "Differences in temporal reasoning." Focaal 2013, no. 66 (June 1, 2013): 25–35. http://dx.doi.org/10.3167/fcl.2013.660103.

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Hoyerswerda, Germany's fastest-shrinking city, faces problems with the future that seem initially unrelated to the past and yet excite manifold conflicting accounts of it. The multiple and conflicting temporal references employed by Hoyerswerdians indicate that the temporal regime of postsocialism is accompanied, if not overcome, by the temporal framework of shrinkage. By reintroducing the analytical domain of the future, I show that local temporal knowledge practices are not historically predetermined by a homogenous postsocialist culture or by particular generational experiences. Rather, they exhibit what I call temporal complexity and temporal flexibility-creative uses of a variety of coexisting temporal references. My ethnographic material illustrates how such expressions of different forms of temporal reasoning structure social relations within and between different generations. Corresponding social groups are not simply divided by age, but are united through shared and heavily disputed negotiations of the post-Cold War era's contemporary crisis.

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Schaeken, Walter, and Philip N. Johnson-Laird. "Strategies in temporal reasoning." Thinking & Reasoning 6, no. 3 (August 2000): 193–219. http://dx.doi.org/10.1080/13546780050114500.

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Crépault, Jacques. "Temporal Reasoning: What Develops?" Psychologica Belgica 33, no. 2 (January 1, 1993): 197. http://dx.doi.org/10.5334/pb.848.

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Nebel, Bernhard, and Hans-Jürgen Bürckert. "Reasoning about temporal relations." Journal of the ACM 42, no. 1 (January 3, 1995): 43–66. http://dx.doi.org/10.1145/200836.200848.

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Bettini, C., and A. Montanari. "Temporal representation and reasoning." Data & Knowledge Engineering 44, no. 2 (February 2003): 139–41. http://dx.doi.org/10.1016/s0169-023x(02)00132-5.

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MORRIS, ROBERT, and LINA KHATIB. "Temporal Representation and Reasoning." Knowledge Engineering Review 12, no. 4 (December 1997): 411–12. http://dx.doi.org/10.1017/s0269888997003081.

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Artificial intelligence research in temporal reasoning focuses on designing automated solutions to complex problems in computation involving time. TIME-97, the 4th International Workshop on Temporal Representation and Reasoning, held in Daytona Beach, Florida — like the three workshops that preceded it — had the objective of creating an international forum for the exchange of information among the many researchers and knowledge engineers who are developing and applying techniques in temporal reasoning.

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Dissertations / Theses on the topic "Temporal reasoning"

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Sripada, Suryanarayana Murthy. "Temporal reasoning in deductive databases." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387841.

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Gago, M. Carmen FernaÌndez. "Efficient control of temporal reasoning." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402682.

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Nilsson, Mikael. "Efficient Temporal Reasoning with Uncertainty." Licentiate thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119409.

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Automated Planning is an active area within Artificial Intelligence. With the help of computers we can quickly find good plans in complicated problem domains, such as planning for search and rescue after a natural disaster. When planning in realistic domains the exact duration of an action generally cannot be predicted in advance. Temporal planning therefore tends to use upper bounds on durations, with the explicit or implicit assumption that if an action happens to be executed more quickly, the plan will still succeed. However, this assumption is often false. If we finish cooking too early, the dinner will be cold before everyone is at home and can eat. Simple Temporal Networks with Uncertainty (STNUs) allow us to model such situations. An STNU-based planner must verify that the temporal problems it generates are executable, which is captured by the property of dynamic controllability (DC). If a plan is not dynamically controllable, adding actions cannot restore controllability. Therefore a planner should verify after each action addition whether the plan remains DC, and if not, backtrack. Verifying dynamic controllability of a full STNU is computationally intensive. Therefore, incremental DC verification algorithms are needed. We start by discussing two existing algorithms relevant to the thesis. These are the very first DC verification algorithm called MMV (by Morris, Muscettola and Vidal) and the incremental DC verification algorithm called FastIDC, which is based on MMV. We then show that FastIDC is not sound, sometimes labeling networks as dynamically controllable when they are not. We analyze the algorithm to pinpoint the cause and show how the algorithm can be modified to correctly and efficiently detect uncontrollable networks. In the next part we use insights from this work to re-analyze the MMV algorithm. This algorithm is pseudo-polynomial and was later subsumed by first an n5 algorithm and then an n4 algorithm. We show that the basic techniques used by MMV can in fact be used to create an n4 algorithm for verifying dynamic controllability, with a new termination criterion based on a deeper analysis of MMV. This means that there is now a comparatively easy way of implementing a highly efficient dynamic controllability verification algorithm. From a theoretical viewpoint, understanding MMV is important since it acts as a building block for all subsequent algorithms that verify dynamic controllability. In our analysis we also discuss a change in MMV which reduces the amount of regression needed in the network substantially. In the final part of the thesis we show that the FastIDC method can result in traversing part of a temporal network multiple times, with constraints slowly tightening towards their final values. As a result of our analysis we then present a new algorithm with an improved traversal strategy that avoids this behavior. The new algorithm, EfficientIDC, has a time complexity which is lower than that of FastIDC. We prove that it is sound and complete.

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Hamlet, I. M. "Assumption based temporal reasoning in medicine." Thesis, University of Sussex, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235351.

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Evans, David Hugh. "An investigation of persistence in temporal reasoning." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267814.

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Tawfik, Ahmed Yassin. "Changing times, an investigation in probabilistic temporal reasoning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq23971.pdf.

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Hostetter, Michael. "Analogical representation in temporal, spatial, and mnemonic reasoning." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03242009-040545/.

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Gao, Feng. "Complex medical event detection using temporal constraint reasoning." Thesis, University of Aberdeen, 2010. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=153271.

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The Neonatal Intensive Care Unit (NICU) is a hospital ward specializing in looking after premature and ill newborn babies. Working in such a busy and complex environment is not easy and sophisticated equipment is used to help the daily work of the medical staff . Computers are used to analyse the large amount of monitored data and extract hidden information, e.g. to detect interesting events. Unfortunately, one group of important events lacks features that are recognizable by computers. This group includes the actions taken by the medical sta , for example two actions related to the respiratory system: inserting an endotracheal tube into a baby’s trachea (ET Intubating) or sucking out the tube (ET Suctioning). These events are very important building blocks for other computer applications aimed at helping the sta . In this research, a strategy for detecting these medical actions based on contextual knowledge is proposed. This contextual knowledge specifies what other events normally occur with each target event and how they are temporally related to each other. The idea behind this strategy is that all medical actions are taken for di erent purposes hence may have di erent procedures (contextual knowledge) for performing them. This contextual knowledge is modelled using a point based framework with special attention given to various types of uncertainty. Event detection consists in searching for consistent matching between a model based on the contextual knowledge and the observed event instances - a Temporal Constraint Satisfaction Problem (TCSP). The strategy is evaluated by detecting ET Intubating and ET Suctioning events, using a specially collected NICU monitoring dataset. The results of this evaluation are encouraging and show that the strategy is capable of detecting complex events in an NICU.

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Shu, I.-hsiang 1979. "Enabling fast flexible planning through incremental temporal reasoning." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/18035.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (leaves 70-71).<br>In order for a team of autonomous agents to successfully complete its mission, the agents must be able to quickly re-plan on the fly as unforeseen events arise in the environment. This requires temporally flexible plans that allow the agent to adapt to execution uncertainties by not overcommitting on time constraints, and a continuous planner that replans at any point when the current plan fails. To achieve both of these requirements, planners must have the ability to reason quickly about timing constraints. This thesis provides a fast incremental algorithm, ITC, for determining the temporal consistency of temporally flexible plans. Additionally, the temporal reasoning capability of ITC is able to return the conflict or the nature of the inconsistency to the planner, such that the planner can resolve inconsistencies quickly and intelligently. The ITC algorithm combines the speed of shortest-path algorithms known to network optimization with the spirit of incremental algorithms such as Incremental A* and those used within truth maintenance systems (TMS). The algorithm has been implemented and integrated into a temporal planner, called Kirk. It has demonstrated an order of magnitude speed increase on cooperative air vehicle scenarios.<br>by I-hsiang Shu.<br>M.Eng.

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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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Books on the topic "Temporal reasoning"

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Stock, Oliviero, ed. Spatial and Temporal Reasoning. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-0-585-28322-7.

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Stock, Oliviero. Spatial and Temporal Reasoning. Dordrecht: Springer, 1997.

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Oliviero, Stock, ed. Spatial and temporal reasoning. Dordrecht: Kluwer Academic Publishers, 1997.

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Ligozat, Gérard. Qualitative Spatial and Temporal Reasoning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118601457.

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Yates, Abigail E. Reasoning with qualitative temporal constraints. Manchester: UMIST, 1996.

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Ligozat, Gérard. Qualitative spatial and temporal reasoning. London, UK: ISTE, 2011.

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1962-, Fisher Michael, Gabbay Dov M. 1945-, and Vila L, eds. Handbook of temporal reasoning in artificial intelligence. Boston: Elsevier, 2005.

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Caluwe, Rita. Spatio-Temporal Databases: Flexible Querying and Reasoning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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J, Egenhofer Max, and Golledge Reginald G. 1937-, eds. Spatial and temporal reasoning in geographic information systems. New York: Oxford University Press, 1998.

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Bettini, Claudio, Sushil Jajodia, and X. Sean Wang. Time Granularities in Databases, Data Mining, and Temporal Reasoning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04228-1.

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Book chapters on the topic "Temporal reasoning"

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Puppe, Frank. "Temporal Reasoning." In Systematic Introduction to Expert Systems, 79–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77971-8_10.

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Goertzel, Ben, Nil Geisweiller, Lucio Coelho, Predrag Janicic, and Cassio Pennachin. "Temporal Reasoning." In Atlantis Thinking Machines, 79–97. Paris: Atlantis Press, 2011. http://dx.doi.org/10.2991/978-94-91216-11-4_5.

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Terenziani, Paolo. "Qualitative Temporal Reasoning." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_287-2.

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Shekhar, Shashi, and Hui Xiong. "Reasoning, Spatio-temporal." In Encyclopedia of GIS, 955. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1091.

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Terenziani, Paolo. "Qualitative Temporal Reasoning." In Encyclopedia of Database Systems, 2225–29. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_287.

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Terenziani, Paolo. "Qualitative Temporal Reasoning." In Encyclopedia of Database Systems, 2953–57. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_287.

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Combi, Carlo, Elpida Keravnou-Papailiou, and Yuval Shahar. "Temporal Modeling and Temporal Reasoning." In Temporal Information Systems in Medicine, 9–44. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6543-1_2.

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Bozzelli, Laura, and César Sánchez. "Visibly Linear Temporal Logic." In Automated Reasoning, 418–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08587-6_33.

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Hustadt, Ullrich, Boris Konev, Alexandre Riazanov, and Andrei Voronkov. "TeMP: A Temporal Monodic Prover." In Automated Reasoning, 326–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25984-8_23.

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Bellomarini, Luigi, Livia Blasi, Markus Nissl, and Emanuel Sallinger. "The Temporal Vadalog System." In Rules and Reasoning, 130–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21541-4_9.

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Conference papers on the topic "Temporal reasoning"

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Liu, Shihao, and Xiaofei Zhou. "Query-Aware Temporal Aggregation Network for Temporal Knowledge Graph Reasoning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888927.

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Miller, David P. "Temporal reasoning." In the 18th conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/318242.318472.

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Koskinen, Eric, and Tachio Terauchi. "Local temporal reasoning." In CSL-LICS '14: JOINT MEETING OF the Twenty-Third EACSL Annual Conference on COMPUTER SCIENCE LOGIC. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2603088.2603138.

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Koenig, A., and E. Crochon. "Temporal reasoning in TRAM." In the second international conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/67312.67384.

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Terenziani, Paolo, and Antonella Andolina. "Probabilistic quantitative temporal reasoning." In SAC 2017: Symposium on Applied Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3019612.3019712.

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Rabin, Marla J., Paul R. Spinrad, and Thomas C. Fall. "Model Based Temporal Reasoning." In 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, edited by Mohan M. Trivedi. SPIE, 1988. http://dx.doi.org/10.1117/12.946973.

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Dixon, Clare, Michael Fisher, Boris Konev, and Alexei Lisitsa. "Practical First-Order Temporal Reasoning." In 2008 15th International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2008. http://dx.doi.org/10.1109/time.2008.15.

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De Giacomo, Giuseppe. "Temporal Reasoning in Bounded Situation Calculus." In 2015 22nd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2015. http://dx.doi.org/10.1109/time.2015.20.

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WEISSER, JR., PAUL, and RANDY HOWIE. "Distributed scheduling and temporal reasoning." In 7th Computers in Aerospace Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-3119.

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Moreno, Marcio, Rodrigo Santos, Wallas Santos, Sandro Fiorini, Reinaldo Silva, and Renato Cerqueira. "Multimedia Search and Temporal Reasoning." In 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS). IEEE, 2019. http://dx.doi.org/10.1109/icis46139.2019.8940351.

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Reports on the topic "Temporal reasoning"

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Bittner, Thomas. Approximate Qualitative Temporal Reasoning. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada465990.

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Gudivada, V. N., and R. Loganantharaj. Temporal Reasoning and Problem Solving. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada248457.

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Swinney, David A., and Edward E. Smith. Temporal and Qualitative Decomposition of Plausible Reasoning. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada290325.

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Swinney, David A., and Edward E. Smith. Temporal and Qualitative Decomposition of Plausible Reasoning. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada253031.

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Swinney, David A., and Edward E. Smith. Temporal and Qualitative Decomposition of Plausible Reasoning. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada275073.

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Lutz, Carsten. Interval-based Temporal Reasoning with General TBoxes. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.109.

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Aus der Motivation: Description Logics (DLs) are a family of formalisms well-suited for the representation of and reasoning about knowledge. Whereas most Description Logics represent only static aspects of the application domain, recent research resulted in the exploration of various Description Logics that allow to, additionally, represent temporal information, see [4] for an overview. The approaches to integrate time differ in at least two important aspects: First, the basic temporal entity may be a time point or a time interval. Second, the temporal structure may be part of the semantics (yielding a multi-dimensional semantics) or it may be integrated as a so-called concrete domain. Examples for multi-dimensional point-based logics can be find in, e.g., [21;29], while multi-dimensional interval-based logics are used in, e.g., [23;2]. The concrete domain approach needs some more explanation. Concrete domains have been proposed by Baader and Hanschke as an extension of Description Logics that allows reasoning about 'concrete qualities' of the entities of the application domain such as sizes, length, or weights of real-worlds objects [5]. Description Logics with concrete domains do usually not use a fixed concrete domain; instead the concrete domain can be thought of as a parameter to the logic. As was first described in [16], if a 'temporal' concrete domain is employed, then concrete domains may be point-based, interval-based, or both.

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Bell, Colin E. Temporal Knowledge Representation and Reasoning for Project Planning. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada196075.

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Borgwardt, Stefan, Marcel Lippmann, and 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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Shastri, Lokendra. Spatio-Temporal Neural Networks for Vision, Reasoning and Rapid Decision Making. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada299746.

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Baader, Franz, Silvio Ghilardi, and Carsten Lutz. LTL over Description Logic Axioms. Technische Universität Dresden, 2008. http://dx.doi.org/10.25368/2022.164.

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Most of the research on temporalized Description Logics (DLs) has concentrated on the case where temporal operators can occur within DL concept descriptions. In this setting, reasoning usually becomes quite hard if rigid roles, i.e., roles whose interpretation does not change over time, are available. In this paper, we consider the case where temporal operators are allowed to occur only in front of DL axioms (i.e., ABox assertions and general concept inclusion axioms), but not inside of concepts descriptions. As the temporal component, we use linear temporal logic (LTL) and in the DL component we consider the basic DL ALC. We show that reasoning in the presence of rigid roles becomes considerably simpler in this setting.

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