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Artykuły w czasopismach na temat „Temporal reasoning”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 9 czerwca 2025

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1

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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2

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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3

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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6

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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10

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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11

Krokhin, Andrei, Peter Jeavons, and Peter Jonsson. "Reasoning about temporal relations." Journal of the ACM 50, no. 5 (September 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 (May 22, 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 (January 1995): 97–119. http://dx.doi.org/10.1080/11663081.1995.10510845.

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Engelfriet, Joeri, and Jan Treur. "Temporal theories of reasoning." Journal of Applied Non-Classical Logics 5, no. 2 (January 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 (July 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 (February 27, 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 consistency on interval constraint network and uses computations based on operations of interval algebra. An example of the application of this algorithm is presented. In the conclusion the prospects for the development of methods of temporal reasoning are considered.
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18

Cai, Bibo, Xiao Ding, Zhouhao Sun, Bing Qin, Ting Liu, Baojun Wang, and Lifeng Shang. "Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 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 this work, we propose to augment LMs with the temporal logic induction ability, which frames the temporal reasoning by defining three modular components: temporal dependency inducer and temporal concept defuzzifier and logic validator. The former two components disentangle the explicit/implicit dependency between temporal concepts across context (before, after, ...) and the specific meaning of fuzzy temporal concepts, respectively, while the validator combines the intermediate reasoning clues for robust contextual reasoning about the temporal concepts. Extensive experimental results on TIMEDIAL, a challenging dataset for temporal reasoning over dialog, show that our method, Logic Induction Enhanced Contextualized TEmporal Reasoning (LECTER), can yield great improvements over the traditional language model for temporal reasoning.
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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 comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on how combining symbolic reasoning with machine learning-based information extraction systems can improve performance. It gives a clear overview of the used methodologies for temporal reasoning, and explains how temporal reasoning can be, and has been successfully integrated into temporal information extraction systems. Based on the distillation of existing work, this survey also suggests currently unexplored research areas. We argue that the level of temporal reasoning that current systems use is still incomplete for the full task of temporal information extraction, and that a deeper understanding of how the various types of temporal information can be integrated into temporal reasoning is required to drive future research in this area.
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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). KDNs combine elements of existing temporal reasoning frameworks with the idea of Bernstein polynomials. The velocity profiles of robots are represented using Bernstein polynomials; and dynamic constraints on these velocity profiles can be converted to linear constraints on the to-be-determined coefficients of their Bernstein polynomials. We study KDNs for their attractive theoretical properties and apply them to the Multi-Agent Path Finding (MAPF) problem with higher-order kinodynamic constraints. We show that our approach is not only scalable but also yields smooth velocity profiles for all robots that can be executed by their controllers.
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21

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

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BALABAN, MIRA, and DAN BRAHA. "Temporal reasoning in process planning." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 2 (April 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 formulation using a reified temporal logic, and discuss the advantages of using such a restricted model. To reflect more realistic process planning encountered in real manufacturing environments, we present a model, called n-TCSP, which is a generalization of the TCSP framework. We envision the proposed temporal reasoning framework as one of the modules in the evolving new intelligent computer-aided process planning.
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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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Dixon, Clare, Boris Konev, Michael Fisher, and Sherly Nietiadi. "Deductive temporal reasoning with constraints." Journal of Applied Logic 11, no. 1 (March 2013): 30–51. http://dx.doi.org/10.1016/j.jal.2012.07.001.

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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 (August 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 (July 1993): 26–29. http://dx.doi.org/10.1145/152947.152954.

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

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

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

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

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

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

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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 (March 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) (May 28, 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 of the reasoning process. Mainly, the developed method for solving the reasoning process is based on the definition of two algorithms: the qualitative sum and the qualitative difference. In addition, here, different instances of the model as well as some practical applications of them are presented.
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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 (April 26, 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, which focuses on the characteristics of relations that evolve over time. HTTR first fuses the relation and temporal information in the knowledge graph, then uses the Householder transformation to obtain an orthogonal matrix about the fused information, and finally defines the orthogonal matrix as the rotation of the head-entity to the tail-entity and calculates the similarity between the rotated vector and the vector representation of the tail entity. In addition, we compare three methods for fusing relational and temporal information. We allow other fusion methods to replace the current one as long as the dimensionality satisfies the requirements. We show that HTTR is able to outperform state-of-the-art methods in temporal knowledge graph reasoning tasks and has the ability to learn and infer all of the four relational patterns over time: symmetric reasoning, antisymmetric reasoning, inversion reasoning, and temporal combination 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 (February 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 when the integers are represented in binary, and PSPACE-complete with a unary representation. To do so, we substantially extend standard results about LTL by introducing a new class of succinct ordinal automata that can encode the interaction between the different quantitative temporal operators.
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38

Chen, Wei, Yuting Wu, Shuhan Wu, Zhiyu Zhang, Mengqi Liao, Youfang Lin, and Huaiyu Wan. "CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 14 (April 11, 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 Temporal Knowledge Extrapolation framework (CognTKE), which introduces a novel temporal cognitive relation directed graph (TCR-Digraph) and performs interpretable global shallow reasoning and local deep reasoning over the TCR-Digraph. Specifically, the proposed TCR-Digraph is constituted by retrieving significant local and global historical temporal relation paths associated with the query. In addition, CognTKE presents the global shallow reasoner and the local deep reasoner to perform global one-hop temporal relation reasoning (System 1) and local complex multi-hop path reasoning (System 2) over the TCR-Digraph, respectively. The experimental results on four benchmark datasets demonstrate that CognTKE achieves significant improvement in accuracy compared to the state-of-the-art baselines and delivers excellent zero-shot reasoning ability.
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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 (January 1, 1993): 311. http://dx.doi.org/10.5334/pb.856.

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ter Meulen, Alice G. B. "Cognitive modelling of human temporal reasoning." Behavioral and Brain Sciences 26, no. 5 (October 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 (August 14, 2012): 210–17. http://dx.doi.org/10.3724/sp.j.1016.2012.00210.

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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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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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Pani, Ashis K., and G. P. Bhattacharjee. "Uncertantty in temporal representation and reasoning." International Journal of Computer Mathematics 73, no. 1 (January 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 (September 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. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.
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46

Shoham, Yoav. "Efficient reasoning about rich temporal domains." Journal of Philosophical Logic 17, no. 4 (November 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 (January 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 (February 1, 2004): 51–77. http://dx.doi.org/10.1093/logcom/14.1.51.

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Bodirsky, M., and H. Chen. "Qualitative Temporal and Spatial Reasoning Revisited." Journal of Logic and Computation 19, no. 6 (June 26, 2009): 1359–83. http://dx.doi.org/10.1093/logcom/exp025.

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

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