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Artykuły w czasopismach na temat „Temporal reasoning”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

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

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2

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

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3

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

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4

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 (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 a

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5

Ringel, Felix. "Differences in temporal reasoning." Focaal 2013, no. 66 (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, the

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6

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

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7

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

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8

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

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9

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

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10

MORRIS, ROBERT, and LINA KHATIB. "Temporal Representation and Reasoning." Knowledge Engineering Review 12, no. 4 (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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11

Krokhin, Andrei, Peter Jeavons, and Peter Jonsson. "Reasoning about temporal relations." Journal of the ACM 50, no. 5 (2003): 591–640. http://dx.doi.org/10.1145/876638.876639.

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12

Guesgen, Hans W., Gérard Ligozat, Jochen Renz, and Rita V. Rodríguez. "Spatial and Temporal Reasoning." Spatial Cognition & Computation 8, no. 1-2 (2008): 1–3. http://dx.doi.org/10.1080/13875860801959547.

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13

Engelfriet, Joeri, and Jan Treur. "Temporal theories of reasoning." Journal of Applied Non-Classical Logics 5, no. 1 (1995): 97–119. http://dx.doi.org/10.1080/11663081.1995.10510845.

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14

Engelfriet, Joeri, and Jan Treur. "Temporal theories of reasoning." Journal of Applied Non-Classical Logics 5, no. 2 (1995): 239–61. http://dx.doi.org/10.1080/11663081.1995.10510858.

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15

Keravnou, Elpida T. "Temporal reasoning in medicine." Artificial Intelligence in Medicine 8, no. 3 (1996): 187–91. http://dx.doi.org/10.1016/0933-3657(95)00032-1.

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16

Santos, Eugene. "Cost-based temporal reasoning." Information Sciences 482 (May 2019): 392–418. http://dx.doi.org/10.1016/j.ins.2019.01.037.

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17

Zuenko, Aleksandr A., and Olga V. Fridman. "Reasoning with temporal constraints." Transactions of the Kоla Science Centre of RAS. Series: Engineering Sciences 14, no. 7/2023 (2024): 43–51. http://dx.doi.org/10.37614/2949-1215.2023.14.7.005.

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The work deals with the organization of temporal reasoning basing on constraint satisfaction methods. The definition of the constraint satisfaction problem and the notion of constraint consistency are given. The possibilities of epresentation of a planning problem as an interval constraint network are considered. As a mathematical apparatus for the formalization of temporal reasining, the Allen’s interval algebra is described, which main operations are composition and the intersection of temporal relations. A path consistency algorithm is given that implements one of the types of local consist

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18

Cai, Bibo, Xiao Ding, Zhouhao Sun, et al. "Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12580–88. http://dx.doi.org/10.1609/aaai.v37i11.26481.

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Understanding temporal commonsense concepts, such as times of occurrence and durations is crucial for event-centric language understanding. Reasoning about such temporal concepts in a complex context requires reasoning over both the stated context and the world knowledge that underlines it. A recent study shows massive pre-trained LM still struggle with such temporal reasoning under complex contexts (e.g., dialog) because they only implicitly encode the relevant contexts and fail to explicitly uncover the underlying logical compositions for complex inference, thus may not be robust enough. In

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19

Leeuwenberg, Artuur, and Marie-Francine Moens. "A Survey on Temporal Reasoning for Temporal Information Extraction from Text." Journal of Artificial Intelligence Research 66 (September 30, 2019): 341–80. http://dx.doi.org/10.1613/jair.1.11727.

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Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensi

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20

Zhang, Han, Neelesh Tiruviluamala, Sven Koenig, and T. K. Satish Kumar. "Temporal Reasoning with Kinodynamic Networks." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 415–25. http://dx.doi.org/10.1609/icaps.v31i1.15987.

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Temporal reasoning is central to Artificial Intelligence (AI) and many of its applications. However, the existing algorithmic frameworks for temporal reasoning are not expressive enough to be applicable to robots with complex kinodynamic constraints typically described using differential equations. For example, while minimum and maximum velocity constraints can be encoded in Simple Temporal Networks (STNs), higher-order kinodynamic constraints cannot be represented in existing frameworks. In this paper, we present a novel framework for temporal reasoning called Kinodynamic Networks (KDNs). KDN

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21

Schaeken, W. "Tense, Aspect, and Temporal Reasoning." Thinking & Reasoning 2, no. 4 (1996): 309–27. http://dx.doi.org/10.1080/135467896394456.

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22

BALABAN, MIRA, and DAN BRAHA. "Temporal reasoning in process planning." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 2 (1999): 91–104. http://dx.doi.org/10.1017/s0890060499132049.

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Computer-aided process planning has been recognized as an important tool for coordinating the different operations involved in making the product. While temporal knowledge is central to the design of efficient and reliable process plans, little attention is given to the integration of process planning and temporal processing and reasoning. To fill the void, we propose in this paper a practical approach, which is inspired by the framework of Temporal Constraint Satisfaction Problem (TCSP), to integrate process planning and temporal reasoning. We show that a TCSP formulation is a subset of a for

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23

Tang, Tong Gao. "Programming in temporal-nonmonotonic reasoning." Journal of Automated Reasoning 7, no. 3 (1991): 383–401. http://dx.doi.org/10.1007/bf00249021.

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24

Dixon, Clare, Boris Konev, Michael Fisher, and Sherly Nietiadi. "Deductive temporal reasoning with constraints." Journal of Applied Logic 11, no. 1 (2013): 30–51. http://dx.doi.org/10.1016/j.jal.2012.07.001.

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25

FIADEIRO, J., and T. MAIBAUM. "Temporal Reasoning over Deontic Specifications." Journal of Logic and Computation 1, no. 3 (1991): 357–95. http://dx.doi.org/10.1093/logcom/1.3.357.

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26

Tawfik, Ahmed Y., and Eric M. Neufeld. "Temporal Reasoning and Bayesian Networks." Computational Intelligence 16, no. 3 (2000): 349–77. http://dx.doi.org/10.1111/0824-7935.00116.

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27

Yampratoom, Ed, and James F. Allen. "Performance of temporal reasoning systems." ACM SIGART Bulletin 4, no. 3 (1993): 26–29. http://dx.doi.org/10.1145/152947.152954.

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28

Smessaert, Hans, and Alice G. B. Ter Meulen. "Temporal Reasoning with Aspectual Adverbs." Linguistics and Philosophy 27, no. 2 (2004): 209–61. http://dx.doi.org/10.1023/b:ling.0000016467.50422.63.

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29

Schaeken, Walter, P. N. Johnson-Laird, and Gery d'Ydewalle. "Mental models and temporal reasoning." Cognition 60, no. 3 (1996): 205–34. http://dx.doi.org/10.1016/0010-0277(96)00708-1.

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30

Schockaert, Steven, and Martine De Cock. "Temporal reasoning about fuzzy intervals." Artificial Intelligence 172, no. 8-9 (2008): 1158–93. http://dx.doi.org/10.1016/j.artint.2008.01.001.

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31

Shoham, Yoav, and Drew McDermott. "Problems in formal temporal reasoning." Artificial Intelligence 36, no. 1 (1988): 49–61. http://dx.doi.org/10.1016/0004-3702(88)90078-1.

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32

van Beek, Peter. "Reasoning about qualitative temporal information." Artificial Intelligence 58, no. 1-3 (1992): 297–326. http://dx.doi.org/10.1016/0004-3702(92)90011-l.

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33

Rodriguez, Rita V., Frank D. Anger, and Kenneth M. Ford. "Temporal reasoning: A relativistic model." International Journal of Intelligent Systems 6, no. 3 (1991): 237–54. http://dx.doi.org/10.1002/int.4550060302.

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34

Dillon, L. K., G. Kutty, P. M. Melliar-Smith, L. E. Moser, and Y. S. Ramakrishna. "Visual Specifications for Temporal Reasoning." Journal of Visual Languages & Computing 5, no. 1 (1994): 61–81. http://dx.doi.org/10.1006/jvlc.1994.1004.

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35

Martínez-Martín, Ester, M. Escrig, and Angel P. del Pobil. "A General Qualitative Spatio-Temporal Model Based on Intervals." JUCS - Journal of Universal Computer Science 18, no. (10) (2012): 1343–78. https://doi.org/10.3217/jucs-018-10-1343.

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Many real-world problems involve qualitative reasoning about space and/or time. Actually, it is an adequate tool for dealing with situations in which information is not sufficiently precise. However, despite its numerous applications, it is difficult for people from outside the field to incorporate the required reasoning techniques into their methods. In this paper, we present a general, easy-to-use framework that integrates and solves the reasoning process of all qualitative models based on intervals. This framework has been divided into: (1) a representation magnitude and (2) the resolution

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36

Zhao, Xiaojuan, Aiping Li, Rong Jiang, Kai Chen, and Zhichao Peng. "Householder Transformation-Based Temporal Knowledge Graph Reasoning." Electronics 12, no. 9 (2023): 2001. http://dx.doi.org/10.3390/electronics12092001.

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Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities and relations on a knowledge graph. However, due to the lack of temporal information representation capability, existing approaches can only model partial relational patterns and they cannot handle temporal combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal knowledge graph Reasoning

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37

DEMRI, STÉPHANE, and DAVID NOWAK. "REASONING ABOUT TRANSFINITE SEQUENCES." International Journal of Foundations of Computer Science 18, no. 01 (2007): 87–112. http://dx.doi.org/10.1142/s0129054107004589.

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We introduce a family of temporal logics to specify the behavior of systems with Zeno behaviors. We extend linear-time temporal logic LTL to authorize models admitting Zeno sequences of actions and quantitative temporal operators indexed by ordinals replace the standard next-time and until future-time operators. Our aim is to control such systems by designing controllers that safely work on ω-sequences but interact synchronously with the system in order to restrict their behaviors. We show that the satisfiability and model-checking for the logics working on ωk-sequences is EXPSPACE-complete wh

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38

Chen, Wei, Yuting Wu, Shuhan Wu, et al. "CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 14 (2025): 14815–23. https://doi.org/10.1609/aaai.v39i14.33624.

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Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these path-based methods primarily focus on local temporal paths appearing in recent times, failing to capture the complex temporal paths in TKG and resulting in the loss of longer historical relations related to the query. Motivated by the Dual Process Theory in cognitive science, we propose a Cognitive

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39

Sougné, Jacques, Anne-Sophie Nyssen, and Véronique De Keyser. "Temporal Reasoning and Reasoning Theories A Case Study in Anaesthesiology." Psychologica Belgica 33, no. 2 (1993): 311. http://dx.doi.org/10.5334/pb.856.

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40

ter Meulen, Alice G. B. "Cognitive modelling of human temporal reasoning." Behavioral and Brain Sciences 26, no. 5 (2003): 623–24. http://dx.doi.org/10.1017/s0140525x03410133.

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Modelling human reasoning characterizes the fundamental human cognitive capacity to describe our past experience and use it to form expectations as well as plan and direct our future actions. Natural language semantics analyzes dynamic forms of reasoning in which the real-time order determines the temporal relations between the described events, when reported with telic simple past-tense clauses. It provides models of human reasoning that could supplement ACT-R models.

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41

WANG, Sheng-Sheng, Da-You LIU, Fang-Ming GU, Qian-Nan LV, and Chang-Ji WEN. "Identity Change Based Spatio-Temporal Reasoning." Chinese Journal of Computers 35, no. 2 (2012): 210–17. http://dx.doi.org/10.3724/sp.j.1016.2012.00210.

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42

Zhang, Jingran, Fumin Shen, Xing Xu, and Heng Tao Shen. "Temporal Reasoning Graph for Activity Recognition." IEEE Transactions on Image Processing 29 (2020): 5491–506. http://dx.doi.org/10.1109/tip.2020.2985219.

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43

Fayek, Haytham M., and Justin Johnson. "Temporal Reasoning via Audio Question Answering." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 2283–94. http://dx.doi.org/10.1109/taslp.2020.3010650.

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44

Pani, Ashis K., and G. P. Bhattacharjee. "Uncertantty in temporal representation and reasoning." International Journal of Computer Mathematics 73, no. 1 (1999): 37–54. http://dx.doi.org/10.1080/00207169908804879.

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45

Chittaro, Luca, and Angelo Montanari. "Trends in temporal representation and reasoning." Knowledge Engineering Review 11, no. 3 (1996): 281–88. http://dx.doi.org/10.1017/s026988890000792x.

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Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S

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46

Shoham, Yoav. "Efficient reasoning about rich temporal domains." Journal of Philosophical Logic 17, no. 4 (1988): 443–74. http://dx.doi.org/10.1007/bf00297513.

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Tétreault, Mario, Bernard Marcos, and Jean Lapointe. "Temporal duration reasoning in qualitative simulation." Artificial Intelligence in Engineering 7, no. 4 (1992): 185–97. http://dx.doi.org/10.1016/0954-1810(92)90012-q.

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48

Combi, C. "Representing and Reasoning about Temporal Granularities." Journal of Logic and Computation 14, no. 1 (2004): 51–77. http://dx.doi.org/10.1093/logcom/14.1.51.

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49

Bodirsky, M., and H. Chen. "Qualitative Temporal and Spatial Reasoning Revisited." Journal of Logic and Computation 19, no. 6 (2009): 1359–83. http://dx.doi.org/10.1093/logcom/exp025.

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50

Muller, Philippe. "Topological Spatio–Temporal Reasoning and Representation." Computational Intelligence 18, no. 3 (2002): 420–50. http://dx.doi.org/10.1111/1467-8640.00196.

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