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Journal articles on the topic 'Modal reasoning'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Bell, Victoria A., and P. N. Johnson-Laird. "A Model Theory of Modal Reasoning." Cognitive Science 22, no. 1 (1998): 25–51. http://dx.doi.org/10.1207/s15516709cog2201_2.

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2

Mišćević, Nenad. "Naturalism and Modal Reasoning." Grazer Philosophische Studien 49 (1994): 149–73. http://dx.doi.org/10.5840/gps1994/954910.

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3

Goldvarg, Yevgeniya, and P. N. Johnson-Laird. "Illusions in modal reasoning." Memory & Cognition 28, no. 2 (2000): 282–94. http://dx.doi.org/10.3758/bf03213806.

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4

Miščević, Nenad. "NATURALISM AND MODAL REASONING." Grazer Philosophische studien 49, no. 1 (1994): 149–73. http://dx.doi.org/10.1163/18756735-90000587.

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5

Britz, Katarina, Thomas Meyer, and Ivan Varzinczak. "Preferential Reasoning for Modal Logics." Electronic Notes in Theoretical Computer Science 278 (November 2011): 55–69. http://dx.doi.org/10.1016/j.entcs.2011.10.006.

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6

Marek, W., and M. Truszczyński. "Modal logic for default reasoning." Annals of Mathematics and Artificial Intelligence 1, no. 1-4 (1990): 275–302. http://dx.doi.org/10.1007/bf01531081.

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7

Ule, Andrej. "Scepticism, context and modal reasoning." Acta Analytica 19, no. 33 (2004): 9–30. http://dx.doi.org/10.1007/s12136-004-1009-4.

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8

Tian, Jia, Xun Chen, and Sheng-Ping Dong. "Multi-Modal Reasoning medical diagnosis system integrated with probabilistic reasoning." International Journal of Automation and Computing 2, no. 2 (2005): 134–43. http://dx.doi.org/10.1007/s11633-005-0134-x.

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9

BENNETT, BRANDON. "Modal Logics for Qualitative Spatial Reasoning." Logic Journal of IGPL 4, no. 1 (1996): 23–45. http://dx.doi.org/10.1093/jigpal/4.1.23.

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10

Tobies, S. "PSPACE Reasoning for Graded Modal Logics." Journal of Logic and Computation 11, no. 1 (2001): 85–106. http://dx.doi.org/10.1093/logcom/11.1.85.

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11

Aiello, M. "Reasoning About Space: The Modal Way." Journal of Logic and Computation 13, no. 6 (2003): 889–920. http://dx.doi.org/10.1093/logcom/13.6.889.

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12

Berkowitz, Marvin W., and Monika Keller. "Transitional Processes in Social Cognitive Development: A Longitudinal Study." International Journal of Behavioral Development 17, no. 3 (1994): 447–67. http://dx.doi.org/10.1177/016502549401700304.

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Microprocesses of stage change were studied by applying Snyder and Feldman's consolidation/transition model to substages and subcontents of Selman's stages of friendship reasoning in a six-year longitudinal study of 97 9-to 15-year-old children. It was hypothesised that individuals exhibiting reasoning above their own modal stages would be more likely to experience a developmental advance in modal reasoning, even when examined at the level of substage and subcontent. This was confirmed; however, the amount of variance in above mode reasoning was not related to development. Finally, controversi

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13

Schumann, Andrew. "Nāgārjunian-Yogācārian Modal Logic versus Aristotelian Modal Logic." Journal of Indian Philosophy 49, no. 3 (2021): 467–98. http://dx.doi.org/10.1007/s10781-021-09470-5.

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AbstractThere are two different modal logics: the logic T assuming contingency and the logic K = assuming logical determinism. In the paper, I show that the Aristotelian treatise On Interpretation (Περί ερμηνείας, De Interpretatione) has introduced some modal-logical relationships which correspond to T. In this logic, it is supposed that there are contingent events. The Nāgārjunian treatise Īśvara-kartṛtva-nirākṛtiḥ-viṣṇoḥ-ekakartṛtva-nirākaraṇa has introduced some modal-logical relationships which correspond to K =. In this logic, it is supposed that there is a logical determinism: each event

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14

Wardle, Grant, and Teo Sušnjak. "Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks." Big Data and Cognitive Computing 9, no. 6 (2025): 149. https://doi.org/10.3390/bdcc9060149.

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Our study investigates how the sequencing of text and image inputs within multi-modal prompts affects the reasoning performance of Large Language Models (LLMs). Through empirical evaluations of three major commercial LLM vendors—OpenAI, Google, and Anthropic—alongside a user study on interaction strategies, we develop and validate practical heuristics for optimising multi-modal prompt design. Our findings reveal that modality sequencing is a critical factor influencing reasoning performance, particularly in tasks with varying cognitive load and structural complexity. For simpler tasks involvin

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15

He, Liqi, Zuchao Li, Xiantao Cai, and Ping Wang. "Multi-Modal Latent Space Learning for Chain-of-Thought Reasoning in Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18180–87. http://dx.doi.org/10.1609/aaai.v38i16.29776.

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Chain-of-thought (CoT) reasoning has exhibited impressive performance in language models for solving complex tasks and answering questions. However, many real-world questions require multi-modal information, such as text and images. Previous research on multi-modal CoT has primarily focused on extracting fixed image features from off-the-shelf vision models and then fusing them with text using attention mechanisms. This approach has limitations because these vision models were not designed for complex reasoning tasks and do not align well with language thoughts. To overcome this limitation, we

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16

Cheng, Zihui, Qiguang Chen, Jin Zhang, et al. "CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23678–86. https://doi.org/10.1609/aaai.v39i22.34538.

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Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and m

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17

Chakraborty, Mihir Kr, Sandip Majumder, and Samarjit Kar. "Rough sets, modal logic and approximate reasoning." International Journal of Approximate Reasoning 176 (January 2025): 109305. http://dx.doi.org/10.1016/j.ijar.2024.109305.

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18

Soler-Toscano, F., D. Fernandez-Duque, and A. Nepomuceno-Fernandez. "A modal framework for modelling abductive reasoning." Logic Journal of IGPL 20, no. 2 (2010): 438–44. http://dx.doi.org/10.1093/jigpal/jzq059.

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19

Ohlbach, Hans Jürgen, and Jana Koehler. "Modal logics, description logics and arithmetic reasoning." Artificial Intelligence 109, no. 1-2 (1999): 1–31. http://dx.doi.org/10.1016/s0004-3702(99)00011-9.

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20

Ben-David, Shai, and Rachel Ben-Eliyahu-Zohary. "A modal logic for subjective default reasoning." Artificial Intelligence 116, no. 1-2 (2000): 217–36. http://dx.doi.org/10.1016/s0004-3702(99)00081-8.

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21

Esteva, Francesc, Pere Garcia, Lluís Godo, and Ricardo Rodríguez. "A modal account of similarity-based reasoning." International Journal of Approximate Reasoning 16, no. 3-4 (1997): 235–60. http://dx.doi.org/10.1016/s0888-613x(96)00126-0.

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22

Hong, Zhiling. "Reasoning on Constrained Epistemic Action Modal Logic." International Journal of Hybrid Information Technology 8, no. 11 (2015): 267–72. http://dx.doi.org/10.14257/ijhit.2015.8.11.22.

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23

Orchard, Dominic, Vilem-Benjamin Liepelt, and Harley Eades III. "Quantitative program reasoning with graded modal types." Proceedings of the ACM on Programming Languages 3, ICFP (2019): 1–30. http://dx.doi.org/10.1145/3341714.

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24

Leasure, David E. "TEMPORAL REASONING WITH THE MODAL LOGIC Z." Computational Intelligence 12, no. 3 (1996): 407–22. http://dx.doi.org/10.1111/j.1467-8640.1996.tb00269.x.

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25

Johnson-Laird, Philip N., and Ruth M. J. Byrne. "Modal reasoning, models, and Manktelow and Over." Cognition 43, no. 2 (1992): 173–82. http://dx.doi.org/10.1016/0010-0277(92)90061-l.

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26

Marcacci, Flavia. "Argumentation and counterfactual reasoning in Parmenides and Melissus." Revista Archai, no. 30 (May 10, 2020): e03004. http://dx.doi.org/10.14195/1984-249x_30_4.

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Parmenides and Melissus employ different deductive styles for their different kinds of argumentation. The former’s poem flows in an interesting sequence of passages: contents foreword, methodological premises, krisis, conclusions and corollaries. The latter, however, organizes an extensive process of deduction to show the characteristics of what is. In both cases, the strength of their argument rests on their deductive form, on the syntactical level of their texts: the formal structure of their reasonings help to identify the features and logical intersections of their thoughts. On the one han

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27

Jüttner, Martin, and Ingo Rentschler. "Imagery in multi-modal object learning." Behavioral and Brain Sciences 25, no. 2 (2002): 197–98. http://dx.doi.org/10.1017/s0140525x0238004x.

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Spatial objects may not only be perceived visually but also by touch. We report recent experiments investigating to what extent prior object knowledge acquired in either the haptic or visual sensory modality transfers to a subsequent visual learning task. Results indicate that even mental object representations learnt in one sensory modality may attain a multi-modal quality. These findings seem incompatible with picture-based reasoning schemas but leave open the possibility of modality-specific reasoning mechanisms.

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28

Shi, Haojun, Suyu Ye, Xinyu Fang, et al. "MuMA-ToM: Multi-modal Multi-Agent Theory of Mind." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 1510–19. https://doi.org/10.1609/aaai.v39i2.32142.

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Understanding people's social interactions in complex real-world scenarios often relies on intricate mental reasoning. To truly understand how and why people interact with one another, we must infer the underlying mental states that give rise to the social interactions, i.e., Theory of Mind reasoning in multi-agent interactions. Additionally, social interactions are often multi-modal -- we can watch people's actions, hear their conversations, and/or read about their past behaviors. For AI systems to successfully and safely interact with people in real-world environments, they also need to unde

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29

Zhang, Zaiyue, Yuefei Sui, Cungen Cao, and Guohua Wu. "A formal fuzzy reasoning system and reasoning mechanism based on propositional modal logic." Theoretical Computer Science 368, no. 1-2 (2006): 149–60. http://dx.doi.org/10.1016/j.tcs.2006.09.021.

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30

Zong, Daoming, and Shiliang Sun. "McOmet: Multimodal Fusion Transformer for Physical Audiovisual Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 6621–29. http://dx.doi.org/10.1609/aaai.v37i5.25813.

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Physical commonsense reasoning is essential for building reliable and interpretable AI systems, which involves a general understanding of the physical properties and affordances of everyday objects, how these objects can be manipulated, and how they interact with others. It is fundamentally a multi-modal task, as physical properties are manifested through multiple modalities, including vision and acoustics. In this work, we present a unified framework, named Multimodal Commonsense Transformer (MCOMET), for physical audiovisual commonsense reasoning. MCOMET has two intriguing properties: i) it

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31

MOSCATI, VINCENZO, LIKAN ZHAN, and PENG ZHOU. "Children's on-line processing of epistemic modals." Journal of Child Language 44, no. 5 (2016): 1025–40. http://dx.doi.org/10.1017/s0305000916000313.

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AbstractIn this paper we investigated the real-time processing of epistemic modals in five-year-olds. In a simple reasoning scenario, we monitored children's eye-movements while processing a sentence with modal expressions of different force (might/must). Children were also asked to judge the truth-value of the target sentences at the end of the reasoning task. Consistent with previous findings (Noveck, 2001), we found that children's behavioural responses were much less accurate compared to adults. Their eye-movements, however, revealed that children did not treat the two modal expressions al

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32

Shen, Xiang, Dezhi Han, Chongqing Chen, Gaofeng Luo, and Zhongdai Wu. "An effective spatial relational reasoning networks for visual question answering." PLOS ONE 17, no. 11 (2022): e0277693. http://dx.doi.org/10.1371/journal.pone.0277693.

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Visual Question Answering (VQA) is a method of answering questions in natural language based on the content of images and has been widely concerned by researchers. The existing research on the visual question answering model mainly focuses on the point of view of attention mechanism and multi-modal fusion. It only pays attention to the visual semantic features of the image in the process of image modeling, ignoring the importance of modeling the spatial relationship of visual objects. We are aiming at the existing problems of the existing VQA model research. An effective spatial relationship r

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33

Zhao, Ruilin, Feng Zhao, Liang Hu, and Guandong Xu. "Graph Reasoning Transformers for Knowledge-Aware Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19652–60. http://dx.doi.org/10.1609/aaai.v38i17.29938.

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Augmenting Language Models (LMs) with structured knowledge graphs (KGs) aims to leverage structured world knowledge to enhance the capability of LMs to complete knowledge-intensive tasks. However, existing methods are unable to effectively utilize the structured knowledge in a KG due to their inability to capture the rich relational semantics of knowledge triplets. Moreover, the modality gap between natural language text and KGs has become a challenging obstacle when aligning and fusing cross-modal information. To address these challenges, we propose a novel knowledge-augmented question answer

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34

ZHANG, Xiao-Ru, Zai-Yue ZHANG, Yue-Fei SUI, and Zhi-Sheng HUANG. "Fuzzy Reasoning Based on First-Order Modal Logic." Journal of Software 19, no. 12 (2009): 3170–78. http://dx.doi.org/10.3724/sp.j.1001.2008.03170.

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35

DEGANI, MICHAEL. "Modal reasoning in Dar es Salaam's power network." American Ethnologist 44, no. 2 (2017): 300–314. http://dx.doi.org/10.1111/amet.12480.

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36

Limón, Yensen, Everardo Bárcenas, and Edgard Benítez-Guerrero. "Reasoning in Context-Aware Systems with Modal Logics." Research in Computing Science 133, no. 1 (2017): 51–61. http://dx.doi.org/10.13053/rcs-133-1-5.

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37

Naumov, Pavel G., and Jia Tao. "A modal logic for reasoning about economic policies." Journal of Logic and Computation 27, no. 1 (2015): 395–412. http://dx.doi.org/10.1093/logcom/exv053.

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38

Siegel, Pierre, and Camilla Schwind. "Modal logic based theory for non-monotonic reasoning." Journal of Applied Non-Classical Logics 3, no. 1 (1993): 73–92. http://dx.doi.org/10.1080/11663081.1993.10510796.

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39

Pedersen, Truls, Sjur Dyrkolbotn, and Thomas Ågotnes. "Reasoning about reasons behind preferences using modal logic." Information Systems Frontiers 17, no. 4 (2014): 713–24. http://dx.doi.org/10.1007/s10796-014-9520-7.

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40

Hartl, Peter. "Modal scepticism, Yablo-style conceivability, and analogical reasoning." Synthese 193, no. 1 (2015): 269–91. http://dx.doi.org/10.1007/s11229-015-0759-4.

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41

Khan, Md Aquil, and Vineeta Singh Patel. "A Simple Modal Logic for Reasoning in Multigranulation Rough Set Model." ACM Transactions on Computational Logic 19, no. 4 (2018): 1–23. http://dx.doi.org/10.1145/3274664.

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42

Li, John M., Amal Ahmed, and Steven Holtzen. "Lilac: A Modal Separation Logic for Conditional Probability." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 148–71. http://dx.doi.org/10.1145/3591226.

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We present Lilac, a separation logic for reasoning about probabilistic programs where separating conjunction captures probabilistic independence. Inspired by an analogy with mutable state where sampling corresponds to dynamic allocation, we show how probability spaces over a fixed, ambient sample space appear to be the natural analogue of heap fragments, and present a new combining operation on them such that probability spaces behave like heaps and measurability of random variables behaves like ownership. This combining operation forms the basis for our model of separation, and produces a log

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43

Kupke, Clemens, Dirk Pattinson, and Lutz Schröder. "Coalgebraic Reasoning with Global Assumptions in Arithmetic Modal Logics." ACM Transactions on Computational Logic 23, no. 2 (2022): 1–34. http://dx.doi.org/10.1145/3501300.

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We establish a generic upper bound ExpTime for reasoning with global assumptions (also known as TBoxes) in coalgebraic modal logics. Unlike earlier results of this kind, our bound does not require a tractable set of tableau rules for the instance logics, so that the result applies to wider classes of logics. Examples are Presburger modal logic, which extends graded modal logic with linear inequalities over numbers of successors, and probabilistic modal logic with polynomial inequalities over probabilities. We establish the theoretical upper bound using a type elimination algorithm. We also pro

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44

Marek, V. Wiktor, and Miroslaw Truszczynski. "More on Modal Aspects of Default Logic1." Fundamenta Informaticae 17, no. 1-2 (1992): 99–116. http://dx.doi.org/10.3233/fi-1992-171-207.

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Investigations of default logic have been so far mostly concerned with the notion of an extension of a default theory. It turns out, however, that default logic is much richer. Namely, there are other natural classes of objects that might be associated with default reasoning. We study two such classes of objects with emphasis on their relations with modal nonmonotonic formalisms. First, we introduce the concept of a weak extension and study its properties. It has long been suspected that there are close connections between default and autoepistemic logics. The notion of weak extension allows u

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45

Hella, Lauri, Antti Kuusisto, Arne Meier, and Jonni Virtema. "Model checking and validity in propositional and modal inclusion logics." Journal of Logic and Computation 29, no. 5 (2019): 605–30. http://dx.doi.org/10.1093/logcom/exz008.

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Abstract Propositional and modal inclusion logic are formalisms that belong to the family of logics based on team semantics. This article investigates the model checking and validity problems of these logics. We identify complexity bounds for both problems, covering both lax and strict team semantics. By doing so, we come close to finalizing the programme that aims to completely classify the complexities of the basic reasoning problems for modal and propositional dependence, independence and inclusion logics.

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46

Kudo, Yasuo, and Tetsuya Murai. "A Modal Characterization of Visibility and Focus in Granular Reasoning." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (2009): 297–303. http://dx.doi.org/10.20965/jaciii.2009.p0297.

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We have proposed two key concepts – focus and visibility – as modalities of modal logic. Scott-Montague models that we have proposed represent properties of visibility and focus and the concept that p is visible as modal sentence vis p and p is clearly visible – or is in focus – as modal sentence cv p.

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47

Fang, Liangda, Kewen Wang, Zhe Wang, and Ximing Wen. "Disjunctive Normal Form for Multi-Agent Modal Logics Based on Logical Separability." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2817–26. http://dx.doi.org/10.1609/aaai.v33i01.33012817.

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Modal logics are primary formalisms for multi-agent systems but major reasoning tasks in such logics are intractable, which impedes applications of multi-agent modal logics such as automatic planning. One technique of tackling the intractability is to identify a fragment called a normal form of multiagent logics such that it is expressive but tractable for reasoning tasks such as entailment checking, bounded conjunction transformation and forgetting. For instance, DNF of propositional logic is tractable for these reasoning tasks. In this paper, we first introduce a notion of logical separabili

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48

Cheng, Yuhao, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. "Cross-modal Graph Matching Network for Image-text Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–23. http://dx.doi.org/10.1145/3499027.

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Image-text retrieval is a fundamental cross-modal task whose main idea is to learn image-text matching. Generally, according to whether there exist interactions during the retrieval process, existing image-text retrieval methods can be classified into independent representation matching methods and cross-interaction matching methods. The independent representation matching methods generate the embeddings of images and sentences independently and thus are convenient for retrieval with hand-crafted matching measures (e.g., cosine or Euclidean distance). As to the cross-interaction matching metho

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49

Garin, Sergei. "Alexander of Aphrodisias on syllogistic reasoning." ΣΧΟΛΗ. Ancient Philosophy and the Classical Tradition 13, no. 1 (2019): 32–47. http://dx.doi.org/10.25205/1995-4328-2019-13-1-32-47.

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The article deals with ancient ideas on the nature of syllogistics on the example of Empire's official Peripatetic philosopher, Alexander of Aphrodisias. We interpret Alexander's position on the syllogistic form as a theory of constant function. Alexander offers a conjunctive and purely formal understanding of the nature of syllogistic necessity. This approach to the modal properties of assertoric judgments differs from Theophrastus’ ontological position, who believed that modal characteristics of assertoric premises are determined by looking to the state-of-affairs to which they refer. Also,

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50

Eger, Markus, Camille Barot, and R. Young. "Merits of a Temporal Modal Logic for Narrative Discourse Generation." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 4 (2021): 23–29. http://dx.doi.org/10.1609/aiide.v11i4.12836.

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Just as there exists varied uses for computational models of narrative, there exists a wide variety of languages aimed at representing stories. A number of them have historic roots in automated generation, for which these languages have to be limited in order to make the generation process computationally feasible. Other are focused on story understanding, with close ties to natural language making many reasoning processes computationally intractable. In this paper, we discuss the trade-off between expressivity and computational complexity of the reasoning process and argue that Impulse, a tem

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