Journal articles: 'Context Reasoning'

1

Pollard, Paul, and Jonathan St B. T. Evans. "Content and Context Effects in Reasoning." American Journal of Psychology 100, no. 1 (1987): 41. http://dx.doi.org/10.2307/1422641.

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MacColl, San. "The Context of Reasoning and Teaching Reasoning." Thinking: The Journal of Philosophy for Children 11, no. 2 (1994): 25–29. http://dx.doi.org/10.5840/thinking199411229.

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McBee, Elexis, Temple Ratcliffe, Lambert Schuwirth, Daniel O’Neill, Holly Meyer, Shelby J. Madden, and Steven J. Durning. "Context and clinical reasoning." Perspectives on Medical Education 7, no. 4 (April 27, 2018): 256–63. http://dx.doi.org/10.1007/s40037-018-0417-x.

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Kofod-Petersen, Anders, and Marius Mikalsen. "Context: Representation and Reasoning. Representing and Reasoning about Context in a Mobile Environment." Revue d'intelligence artificielle 19, no. 3 (June 1, 2005): 479–98. http://dx.doi.org/10.3166/ria.19.479-498.

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Byrne, Ruth M. J., Simon J. Handley, and Philip N. Johnson-Laird. "Reasoning from Suppositions." Quarterly Journal of Experimental Psychology Section A 48, no. 4 (November 1995): 915–44. http://dx.doi.org/10.1080/14640749508401423.

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Two experiments investigated inferences based on suppositions. In Experiment 1, the subjects decided whether suppositions about individuals’ veracity were consistent with their assertions—for example, whether the supposition “Ann is telling the truth and Beth is telling a lie”, is consistent with the premises: “Ann asserts: I am telling the truth and Beth is telling the truth. Beth asserts: Ann is telling the truth”. It showed that these inferences are more difficult than ones based on factual premises: “Ann asserts: I live in Dublin and Beth lives in Dublin”. There was no difference between problems about truthtellers and liars, who always told the truth or always lied, and normals, who sometimes told the truth and sometimes lied. In Experiment 2, the subjects made inferences about factual matters set in three contexts: a truth-inducing context in which friends confided their personality characteristics, a lie-inducing context in which business rivals advertised their products, and a neutral context in which computers printed their program characteristics. Given the supposition that the individuals were lying, it was more difficult to make inferences in a truth-inducing context than in the other two contexts. We discuss the implications of our results for everyday reasoning from suppositions, and for theories of reasoning based on models or inference rules.

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Firouzi, Ramin, Rahim Rahmani, and Theo Kanter. "Context-based Reasoning through Fuzzy Logic for Edge Intelligence." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 17–25. http://dx.doi.org/10.5383/juspn.15.01.003.

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With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

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Bravo, Maricela, José A. Reyes-Ortiz, Isabel Cruz-Ruiz, Ariadna Gutiérrez-Rosales, and Josué Padilla-Cuevas. "Ontology for Academic Context Reasoning." Procedia Computer Science 141 (2018): 175–82. http://dx.doi.org/10.1016/j.procs.2018.10.165.

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8

Murray, Thomas H. "Moral Reasoning in Social Context." Journal of Social Issues 49, no. 2 (July 1993): 185–200. http://dx.doi.org/10.1111/j.1540-4560.1993.tb00927.x.

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9

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

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Barrouillet, Pierre, and Jean-François Lecas. "Content and context effects in children's and adults' conditional reasoning." Quarterly Journal of Experimental Psychology Section A 55, no. 3 (August 2002): 839–54. http://dx.doi.org/10.1080/02724980143000587.

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We have recently shown that children interpret conditional sentences with binary terms (e.g., male/female) in both the antecedent and the consequent as biconditionals (Barrouillet & Lecas, 1998). We hypothesized that the same effect can be obtained with conditionals that do not contain binary terms provided that they are embedded in a context that restricts to only two the possible values on both the antecedent and the consequent. In the present experiment, we asked 12-year-old children, 15-year-old children, and adults to draw conclusions from conditional syllogisms that involved three types of conditional sentence: (1) conditionals with binary terms (BB), (2) conditionals with non-binary terms (NN), and (3) conditionals with non-binary terms embedded in a restrictive context (NNR). As we predicted, BB conditionals elicited more biconditional response patterns than did NN conditionals in all age groups. On the other hand, manipulating the context had the same effect in children but not in adults. Content and context constraints on conditional reasoning along with developmental issues are discussed within the framework of the mental models theory.

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Kuhn, Deanna, Erin Phelps, and Joseph Walters. "Correlational reasoning in an everyday context." Journal of Applied Developmental Psychology 6, no. 1 (January 1985): 85–97. http://dx.doi.org/10.1016/0193-3973(85)90018-8.

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Roberts, Maxwell J., Heather Welfare, Doreen P. Livermore, and Alice M. Theadom. "Context, visual salience, and inductive reasoning." Thinking & Reasoning 6, no. 4 (November 2000): 349–74. http://dx.doi.org/10.1080/135467800750038175.

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Schulz, Stefan, and Thomas R. Roth-Berghofer. "Context-based Retrieval for Explainable Reasoning." Revue d'intelligence artificielle 19, no. 3 (June 1, 2005): 519–35. http://dx.doi.org/10.3166/ria.19.519-535.

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Bray, Wendy S., and Tanya Vik Blais. "Stimulating Base-Ten Reasoning with Context." Teaching Children Mathematics 24, no. 2 (October 2017): 120–27. http://dx.doi.org/10.5951/teacchilmath.24.2.0120.

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Dean, Robyn K., and Robert Q. Pollard. "Context-based Ethical Reasoning in Interpreting." Interpreter and Translator Trainer 5, no. 1 (March 2011): 155–82. http://dx.doi.org/10.1080/13556509.2011.10798816.

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Baeten, Elizabeth. "A Naturalized Context of Moral Reasoning." Pluralist 4, no. 2 (2009): 63–81. http://dx.doi.org/10.1353/plu.0.0021.

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Bolger, Fergus, and Nigel Harvey. "Context-Sensitive Heuristics in Statistical Reasoning." Quarterly Journal of Experimental Psychology Section A 46, no. 4 (November 1993): 779–811. http://dx.doi.org/10.1080/14640749308401039.

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Previous work has shown that people use anchor-and-adjust heuristics to forecast future data points from previous ones in the same series. We report three experiments that show that they use different versions of this heuristic for different types of series. To forecast an untrended series, our subjects always took a weighted average of the long-term mean of the series and the last data point. In contrast, the way that they forecast a trended series depended on the serial dependences in it. When these were low, people forecast by adding a proportion of the last difference in the series to the last data point. When stronger serial dependences made this difference less similar to the next one, they used a version of the averaging heuristic that they employed for untrended series. This could take serial dependences into account and included a separate component for trend. These results suggest that people use a form of the heuristic that is well adapted to the nature of the series that they are forecasting. However, we also found that the size of their adjustments tended to be suboptimal. They overestimated the degree of serial dependence in the data but underestimated trends. This biased their forecasts.

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18

Wu, Jingjing. "Justifying Particular Reasoning in a Legal Context." Informal Logic 40, no. 3 (August 29, 2020): 423–41. http://dx.doi.org/10.22329/il.v40i30.5994.

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Particular reasoning is arguably the most common type of legal reasoning. Neil MacCormick proposed that, in a legal context, justifiable particular reasoning has to be universalizable. This paper aims to: (1) investigate MacCormick’s thesis; (2) explain how a particular can ever be universal by drawing inspiration from Scott Brewer’s formula on reasoning by analogy; (3) further comprehend MacCormick’s thesis by considering some of the arguments advanced by its opponents; (4) use the ‘pilot-judgement procedure’ developed by the European Court of Human Rights as an example to illustrate the relevance of the universalizable particular thesis in today’s legal practices.

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Stein, Gary, and Avelino J. Gonzalez. "Learning in context: enhancing machine learning with context-based reasoning." Applied Intelligence 41, no. 3 (July 3, 2014): 709–24. http://dx.doi.org/10.1007/s10489-014-0550-0.

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Deeba K. and Saravanaguru R. A. K. "Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm." International Journal of e-Collaboration 17, no. 4 (October 2021): 41–59. http://dx.doi.org/10.4018/ijec.2021100104.

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Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

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Domberg, Andreas, Michael Tomasello, and Bahar Köymen. "Collaborative reasoning in the context of group competition." PLOS ONE 16, no. 2 (February 5, 2021): e0246589. http://dx.doi.org/10.1371/journal.pone.0246589.

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A key skill in collaborative problem-solving is to communicate and evaluate reasons for proposals to arrive at the decision benefiting all group members. Although it is well-documented that collaborative contexts facilitate young children’s reasoning, less is known about whether competition with other groups contributes to children’s collaborative reasoning. We investigated whether between-group competition facilitates children’s within-group collaborative reasoning, regarding their production of reasons and their use oftransacts, communicative acts that operate on one another’s proposals and reasoning. We presented 5- and 7-year-old peer dyads with two collaborative problem-solving tasks (decorating a zoo and a dollhouse). In one task, children competed against another group (the competitive condition); whereas in the other task, they did not (non-competitive condition). Our results suggest that children’s sensitivity to group competition as reflected in their reasoning changed depending on the task. When they decorated a house, they produced more transacts in the competitive condition than in the non-competitive condition; whereas when they decorated a zoo, this pattern was reversed. Thus, our results highlight that group competition did not influence children’s collaborative reasoning consistently across different contexts.

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22

KAZZAZ, M. Mohanned, and Marek RYCHLÝ. "Ontology-Based Context Modelling and Reasoning in the Web Service Migration Framework." Acta Electrotechnica et Informatica 13, no. 4 (December 1, 2013): 5–12. http://dx.doi.org/10.15546/aeei-2013-0042.

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23

BRATMAN, MICHAEL E. "Practical Reasoning and Acceptance in a Context." Mind 101, no. 401 (1992): 1–16. http://dx.doi.org/10.1093/mind/101.401.1.

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24

Besnard, Philippe, and Torsten Schaub. "AN APPROACH TO CONTEXT-BASED DEFAULT REASONING." Fundamenta Informaticae 23, no. 2,3,4 (1995): 175–223. http://dx.doi.org/10.3233/fi-1995-232342.

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25

Oh, Yoosoo. "A Survey on Ontologies for Context Reasoning." Indian Journal of Science and Technology 8, no. 1 (January 20, 2015): 1–4. http://dx.doi.org/10.17485/ijst/2015/v8i26/84648.

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Lehmann, Jos, Ivan José Varzinczak, and Alan Bundy. "Reasoning with Context in the Semantic Web." Journal of Web Semantics 12-13 (April 2012): 1–2. http://dx.doi.org/10.1016/j.websem.2011.12.004.

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Wang, Shangguang, Hongke Zhang, Qibo Sun, Zouhua Zou, and Fangchun Yang. "User Context Reasoning for Web Service Selection." Advanced Science Letters 7, no. 1 (March 30, 2012): 576–78. http://dx.doi.org/10.1166/asl.2012.2729.

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Suh, Donghyok, and Kunsoo Oh. "Context Inference Including Cause Reasoning and Prediction." International Journal of Control and Automation 6, no. 5 (October 31, 2013): 31–44. http://dx.doi.org/10.14257/ijca.2013.6.5.04.

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Pratt, Dave, Janet Ainley, Phillip Kent, Ralph Levinson, Cristina Yogui, and Ramesh Kapadia. "Role of Context in Risk-Based Reasoning." Mathematical Thinking and Learning 13, no. 4 (October 2011): 322–45. http://dx.doi.org/10.1080/10986065.2011.608346.

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Michalakis, Konstantinos, Yannis Christodoulou, George Caridakis, Yorghos Voutos, and Phivos Mylonas. "A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces." Applied Sciences 11, no. 13 (June 22, 2021): 5770. http://dx.doi.org/10.3390/app11135770.

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The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.

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Carstensen, Alexandra, Jing Zhang, Gail D. Heyman, Genyue Fu, Kang Lee, and Caren M. Walker. "Context shapes early diversity in abstract thought." Proceedings of the National Academy of Sciences 116, no. 28 (June 24, 2019): 13891–96. http://dx.doi.org/10.1073/pnas.1818365116.

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Early abstract reasoning has typically been characterized by a “relational shift,” in which children initially focus on object features but increasingly come to interpret similarity in terms of structured relations. An alternative possibility is that this shift reflects a learned bias, rather than a typical waypoint along a universal developmental trajectory. If so, consistent differences in the focus on objects or relations in a child’s learning environment could create distinct patterns of relational reasoning, influencing the type of hypotheses that are privileged and applied. Specifically, children in the United States may be subject to culture-specific influences that bias their reasoning toward objects, to the detriment of relations. In experiment 1, we examine relational reasoning in a population with less object-centric experience—3-y-olds in China—and find no evidence of the failures observed in the United States at the same age. A second experiment with younger and older toddlers in China (18 to 30 mo and 30 to 36 mo) establishes distinct developmental trajectories of relational reasoning across the two cultures, showing a linear trajectory in China, in contrast to the U-shaped trajectory that has been previously reported in the United States. In a third experiment, Chinese 3-y-olds exhibit a bias toward relational solutions in an ambiguous context, while those in the United States prefer object-based solutions. Together, these findings establish population-level differences in relational bias that predict the developmental trajectory of relational reasoning, challenging the generality of an initial object focus and suggesting a critical role for experience.

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32

Nagle, Courtney R., and Jodie L. Styers. "Putting Mathematical Tasks into Context." Mathematics Teacher 109, no. 3 (October 2015): 206–13. http://dx.doi.org/10.5951/mathteacher.109.3.0206.

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33

Ko, Kwang-Eun, and Kwee-Bo Sim. "Context Aware System based on Bayesian Network driven Context Reasoning and Ontology Context Modeling." International Journal of Fuzzy Logic and Intelligent Systems 8, no. 4 (December 1, 2008): 254–59. http://dx.doi.org/10.5391/ijfis.2008.8.4.254.

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34

Alexander, Patricia A., Sophie Jablansky, Lauren M. Singer, and Denis Dumas. "Relational Reasoning." Policy Insights from the Behavioral and Brain Sciences 3, no. 1 (January 5, 2016): 36–44. http://dx.doi.org/10.1177/2372732215622029.

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This article addresses two goals. First, it considers the nature and importance of relational reasoning, the ability to discern meaningful patterns within informational streams. Second, it examines four principles about relational reasoning derived from the empirical literature. Specifically, we argue that relational reasoning is foundational and pervasive; varies as a consequence of age, domain, and context; can be measured in diverse ways; and is malleable and teachable. Evidence supports each of these principles, and the implications for educational policies and practices are weighed.

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CHEN, HARRY, TIM FININ, and ANUPAM JOSHI. "An ontology for context-aware pervasive computing environments." Knowledge Engineering Review 18, no. 3 (September 2003): 197–207. http://dx.doi.org/10.1017/s0269888904000025.

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This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.

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Pacheco, Maria Leonor, and Dan Goldwasser. "Modeling Content and Context with Deep Relational Learning." Transactions of the Association for Computational Linguistics 9 (February 2021): 100–119. http://dx.doi.org/10.1162/tacl_a_00357.

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Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

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Ko, Kwang-Eun, In-Hun Jang, and Kwee-Bo Sim. "Context-based Service Reasoning Model Based on User Environment Information." Journal of Korean Institute of Intelligent Systems 17, no. 7 (December 25, 2007): 907–12. http://dx.doi.org/10.5391/jkiis.2007.17.7.907.

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38

Xu, Nan, Wei Shi Zhang, Hua Dong Yang, Xiu Guo Zhang, and Xing Xing. "CACOnt: A Ontology-Based Model for Context Modeling and Reasoning." Applied Mechanics and Materials 347-350 (August 2013): 2304–10. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2304.

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In this paper, we present a general and extensible context-aware computing ontology (CACOnt) for modeling context and providing inference mechanisms. CACOnt provides not only the generic context ontologies for capturing basic concepts about context, but also the extensibility for adding domain-specific ontologies in a hierarchical manner. CACOnt facilitates the context reasoning capabilities by providing semantic logics which is possible to combine with rule-based systems. However, the set of rules cannot entirely cover the domain of contexts, we present a semantic similarity-based rule matching algorithm as the solution to this problem.

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Indrawan, Muhammad Gita. "NEW VENTURES CREATION AND JUSTIFICATION IN A GLOBAL CONTEXT." Business and Entrepreneurial Review 11, no. 2 (July 9, 2017): 155. http://dx.doi.org/10.25105/ber.v11i2.1932.

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We argue that creating novel ventures consists of inductive analogical or metaphorical reasoning, which generates a platform for the creation and commercialization of novel ventures andfacilities the comprehension and justification of a vent lire. We argue that such inductive reasoning is shaped by two determinants (the applicability of prior entrepreneurial experience and the motivation to resolve uncertainly and acquire legitimacy) that interrelate to predict and explain pattern of analogical and metaphorical reasoning by which novice and experienced entrepreneurs construct meaning for themselves as well as others in the early stages of creating a venture

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Mancini, Francesco, and Amella Gangemi. "Role of Responsibility in Conditional Reasoning." Psychological Reports 91, no. 1 (August 2002): 275–88. http://dx.doi.org/10.2466/pr0.2002.91.1.275.

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A series of recent studies showed that facilitation on the Wason Selection Task could be produced by perceived utilities. The present work was aimed at testing whether a similar factor could also be involved in human reasoning performance in the context of responsibility. We supposed that the motivation of the subject assuming responsibility is affected by normative goals. These goals prescribe the actions and the results to be achieved, also considering the different social roles. In this experiment the responses of different groups of subjects ( N = 270) to a selection task were compared in two different conditions involving different responsibility contexts. The results show that the subjects' strategies in searching for possible violators depended on the condition (responsibility vs no responsibility). In particular, only in the context of responsibility were the performances elicited by conditional rules characterised by a falsification strategy.

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Cohn, Ellen S., and Susan O. White. "The Relationship between Legal Reasoning and Behavioral Context." Droit et société 19, no. 1 (1991): 389–408. http://dx.doi.org/10.3406/dreso.1991.1124.

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Bettini, Claudio, Oliver Brdiczka, Karen Henricksen, Jadwiga Indulska, Daniela Nicklas, Anand Ranganathan, and Daniele Riboni. "A survey of context modelling and reasoning techniques." Pervasive and Mobile Computing 6, no. 2 (April 2010): 161–80. http://dx.doi.org/10.1016/j.pmcj.2009.06.002.

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Riboni, Daniele, and Claudio Bettini. "COSAR: hybrid reasoning for context-aware activity recognition." Personal and Ubiquitous Computing 15, no. 3 (August 14, 2010): 271–89. http://dx.doi.org/10.1007/s00779-010-0331-7.

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Kempert, Sebastian, and Ilonca Hardy. "Children’s scientific reasoning in the context of bilingualism." International Journal of Bilingualism 19, no. 6 (April 10, 2014): 646–64. http://dx.doi.org/10.1177/1367006914527803.

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45

RICHARDSON, KEN. "REASONING WITH RAVEN - IN AND OUT OF CONTEXT." British Journal of Educational Psychology 61, no. 2 (June 1991): 129–38. http://dx.doi.org/10.1111/j.2044-8279.1991.tb00969.x.

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Shimoyama, Hitoshi, Masao Aihara, Hidenao Fukuyama, Kazuo Hashikawa, Kakurou Aoyagi, Elkhonon Goldberg, and Shinpei Nakazawa. "Context-dependent reasoning in a cognitive bias task." Brain and Development 26, no. 1 (January 2004): 37–42. http://dx.doi.org/10.1016/s0387-7604(03)00092-5.

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Loughlin, Michael. "Editorial introduction: Decision making, reasoning, context, and perspective." Journal of Evaluation in Clinical Practice 26, no. 2 (April 2020): 387–88. http://dx.doi.org/10.1111/jep.13381.

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Limón, Yensen, Everardo Bárcenas, and Edgard Benítez-Guerrero. "Reasoning in Context-Aware Systems with Modal Logics." Research in Computing Science 133, no. 1 (December 31, 2017): 51–61. http://dx.doi.org/10.13053/rcs-133-1-5.

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49

Helwig, Charles C., Marie S. Tisak, and Elliot Turiel. "Children's Social Reasoning in Context: Reply to Gabennesch." Child Development 61, no. 6 (December 1990): 2068. http://dx.doi.org/10.2307/1130860.

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Kamide, Norihiro, and Yishui Zhu. "Reasoning about Context Information in Cloud Computing Environments." Journal of Software Engineering and Applications 05, no. 11 (2012): 944–51. http://dx.doi.org/10.4236/jsea.2012.531109.

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Journal articles on the topic 'Context Reasoning'

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