Academic literature on the topic 'Spatial reasoning'

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Journal articles on the topic "Spatial reasoning"

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Byrne, Ruth M. J., and P. N. Johnson-Laird. "Spatial reasoning." Journal of Memory and Language 28, no. 5 (October 1989): 564–75. http://dx.doi.org/10.1016/0749-596x(89)90013-2.

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Liu, Fangyu, Guy Emerson, and Nigel Collier. "Visual Spatial Reasoning." Transactions of the Association for Computational Linguistics 11 (2023): 635–51. http://dx.doi.org/10.1162/tacl_a_00566.

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Abstract Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: The human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs’ by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.1
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Pradana, Lingga Nico. "Spatial visualization as the strongest spatial reasoning autistic student." International Journal of Special Education (IJSE) 38, no. 2 (October 27, 2023): 26–33. http://dx.doi.org/10.52291/ijse.2023.38.19.

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Abstract spatial reasoning is the strongest reasoning of autistic students. In this study, we want to identify the spatial reasoning of autistic students based on the categories of spatial reasoning (spatial visualization, mental rotation, and spatial orientation) and level of reasoning (abstract and concrete). A total of 46 autistic students and 46 non-autistic students participated as subjects. All subjects were given 6 types of spatial reasoning tests which included all three categories and levels of spatial reasoning. The results that autistic students have an advantage in the abstract spatial visualization category while non-autistic students have an advantage in the concrete spatial visualization category. These results indicate specifically that abstract spatial visualization is the strongest spatial reasoning possessed by autistic students. Thus, this study provides a new picture of students' spatial reasoning ability in terms of the reasoning category and the level of reasoning.
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Thom, Jennifer S. "All about… spatial reasoning." Nursery World 2018, no. 1 (January 8, 2018): 21–25. http://dx.doi.org/10.12968/nuwa.2018.1.21.

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Yoon, K. B., and R. D. Coyne. "Reasoning about spatial constraints." Environment and Planning B: Planning and Design 19, no. 3 (1992): 243–66. http://dx.doi.org/10.1068/b190243.

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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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Hornbuckle, Susan F., Latanya Gobin, and Stephanie N. Thurman. "Spatial Reasoning: Improvement Of Imagery And Abilities In Sophomore Organic Chemistry. Perspective To Enhance Student Learning." Contemporary Issues in Education Research (CIER) 7, no. 1 (December 30, 2013): 45. http://dx.doi.org/10.19030/cier.v7i1.8311.

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Spatial reasoning has become a demanded skill for students pursuing a science emphasis to compete with the dynamic growth of our professional society. The ability to reason spatially includes explorations in memory recollection and problem solving capabilities as well as critical thinking and reasoning skills. With these advancements, educational requirements seek reassurance in that efficient learning skills can be embedded in science curricula. Organic chemistry is a source in which abilities such as spatial reasoning can benefit students intending to thrive in scientific communities. This study of instructional development in organic chemistry seeks to determine if spatial reasoning can be improved by exposing stereo-chemical techniques from the Purdue Visualization Rotation Test (ROT) to students who have not taken organic chemistry and have science oriented educational goals. We sought to determine two purposes: to gauge how much spatial reasoning a student possessed prior to the course, and to test if spatial ability can be improved in students who present low ability over the span of the three month course. Results show significance in that spatial reasoning can be improved through statistical analysis in students who showed below average qualities.
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Callan, Mitchell J., Joshua Moreton, and Gethin Hughes. "Immanent Justice Reasoning by Spatial Proximity." Social Psychological and Personality Science 12, no. 1 (February 19, 2020): 25–33. http://dx.doi.org/10.1177/1948550619893969.

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Immanent justice reasoning involves causally attributing someone’s bad outcome to their prior immoral actions. Building on the idea that causality is mentally linked with spatial proximity, we investigated whether such reasoning might lead participants to spatially bind together immoral actions and bad outcomes. Across four experiments ( N = 553, Mechanical Turk workers), participants positioned sentences describing other people’s bad (vs. good) outcomes closer in space to previous immoral behaviors. This effect was observed both when the position of the initial action remained in a fixed location and when it “chased” the outcome across the screen. Importantly, we also found that this spatial positioning of immoral actions and bad outcomes is mediated by perceived deservingness of the outcome and is not merely due to perceived similarity of events. These findings suggest that perceived deservingness biases the spatial proximity of representations of others’ random bad outcomes and their prior immoral actions.
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Shumway, Jessica F. "Building Bridges to Spatial Reasoning." Teaching Children Mathematics 20, no. 1 (August 2013): 44–51. http://dx.doi.org/10.5951/teacchilmath.20.1.0044.

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VanderPlas, Susan, and Heike Hofmann. "Spatial Reasoning and Data Displays." IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (January 31, 2016): 459–68. http://dx.doi.org/10.1109/tvcg.2015.2469125.

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Dissertations / Theses on the topic "Spatial reasoning"

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Travers, Anthony J. "Interval-based qualitative spatial reasoning." Thesis, Curtin University, 1998. http://hdl.handle.net/20.500.11937/1086.

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The role of spatial reasoning in the development of systems in the domain of Artificial Intelligence is increasing. One particular approach, qualitative spatial reasoning, investigates the usage of abstract representation to facilitate the representation of and the reasoning with spatial information.This thesis investigates the usage of intervals along global axes as the under-lying representational and reasoning mechanism for a spatial reasoning system. Aspects that are unique to representing spatial information (flow and multi-dimensionality) are used to provide a method for classifying relations between objects at multiple levels of granularity. The combination of these two mechanisms (intervals and classification) provide the basis for the development of a querying system that allows qualitative queries about object relations in multi-dimensional space to be performed upon the representation.The second issue examined by this thesis is the problem of representing intervals when all the interval relations may not be known precisely. A three part solution is proposed. The first shows how the simplest situation, where all relations are explicit and primitive, can be represented and integrated with the above mentioned querying system. The second situation demonstrates how, for interval relations that are primitive but are not all explicitly known, an effective point based representation may be constructed. Finally, when relations between intervals are disjunctions of possible primitive interval relations, a representation is presented which allows solutions to queries to be constructed from consistent data.Our contribution is two-fold:1. a method of classifying the spatial relations and the means of querying these relations;2. a process of efficiently representing incomplete interval information and the means of efficiently querying this information.The work presented in this thesis demonstrates the utility of a multi-dimensional qualitative spatial reasoning system based upon intervals. It also demonstrates how an interval representation may be constructed for datasets that have variable levels of information about relationships between intervals represented in the dataset.
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Tellex, Stefanie 1980. "Natural language and spatial reasoning." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61937.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 109-112).
Making systems that understand language has long been a dream of artificial intelligence. This thesis develops a model for understanding language about space and movement in realistic situations. The system understands language from two real-world domains: finding video clips that match a spatial language description such as "People walking through the kitchen and then going to the dining room" and following natural language commands such as "Go down the hall towards the fireplace in the living room." Understanding spatial language expressions is a challenging problem because linguistic expressions, themselves complex and ambiguous, must be connected to real-world objects and events. The system bridges the gap between language and the world by modeling the meaning of spatial language expressions hierarchically, first capturing the semantics of spatial prepositions, and then composing these meanings into higher level structures. Corpus-based evaluations of how well the system performs in different, realistic domains show that the system effectively and robustly understands spatial language expressions.
by Stefanie Anne Tellex.
Ph.D.
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Travers, Anthony J. "Interval-based qualitative spatial reasoning." Curtin University of Technology, School of Computing, 1998. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=9539.

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The role of spatial reasoning in the development of systems in the domain of Artificial Intelligence is increasing. One particular approach, qualitative spatial reasoning, investigates the usage of abstract representation to facilitate the representation of and the reasoning with spatial information.This thesis investigates the usage of intervals along global axes as the under-lying representational and reasoning mechanism for a spatial reasoning system. Aspects that are unique to representing spatial information (flow and multi-dimensionality) are used to provide a method for classifying relations between objects at multiple levels of granularity. The combination of these two mechanisms (intervals and classification) provide the basis for the development of a querying system that allows qualitative queries about object relations in multi-dimensional space to be performed upon the representation.The second issue examined by this thesis is the problem of representing intervals when all the interval relations may not be known precisely. A three part solution is proposed. The first shows how the simplest situation, where all relations are explicit and primitive, can be represented and integrated with the above mentioned querying system. The second situation demonstrates how, for interval relations that are primitive but are not all explicitly known, an effective point based representation may be constructed. Finally, when relations between intervals are disjunctions of possible primitive interval relations, a representation is presented which allows solutions to queries to be constructed from consistent data.Our contribution is two-fold:1. a method of classifying the spatial relations and the means of querying these relations;2. a process of efficiently representing incomplete interval information and the means of efficiently querying this information.The work presented ++
in this thesis demonstrates the utility of a multi-dimensional qualitative spatial reasoning system based upon intervals. It also demonstrates how an interval representation may be constructed for datasets that have variable levels of information about relationships between intervals represented in the dataset.
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Ramalingam, Chitra. "Modeling Multiple Granularities of Spatial Objects." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/RamalingamC2002.pdf.

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Vasardani, Maria. "Qualitative Spatial Reasoning with Holed Regions." Fogler Library, University of Maine, 2010. http://www.library.umaine.edu/theses/pdf/VasardaniM2009.pdf.

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Banerjee, Bonny. "Spatial problem solving for diagrammatic reasoning." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1194455860.

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Antonopoulou, Paraskevi. "Spatial descriptions and verbal reasoning problems." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390915.

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Lazarovski, Daniel. "Extending the Stream Reasoning in DyKnow with Spatial Reasoning in RCC-8." Thesis, Linköpings universitet, KPLAB - Laboratoriet för kunskapsbearbetning, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75885.

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Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS.
Collaborative Unmanned Aircraft Systems (CUAS)
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El-Geresy, Baher. "Qualitative representation and reasoning for spatial and spatio-temporal systems." Thesis, University of South Wales, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403330.

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Peng, Jian. "Rule-based spatial reasoning for robot planning." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315818.

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Books on the topic "Spatial reasoning"

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

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Liu, Jiming, and Laeeque K. Daneshmend. Spatial Reasoning and Planning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18879-4.

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Pierre, Sharon D. La. Spatial reasoning and adults. Edited by Fellenz Robert A and Montana State University (Bozeman, Mont.). Center for Adult Learning Research. Bozeman, Mont: Center for Adult Learning Research, Montana State University, 1988.

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Su-shing, Chen, ed. Advances in spatial reasoning. Norwood, N.J: Ablex Pub. Corp., 1990.

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

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

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Su-shing, Chen, ed. Advances in spatial reasoning. Norwood, N.J: Ablex Publishing Corporation, 1990.

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

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Spatial reasoning for effective GIS. Fort Collins, Colo: GIS World Books, 1995.

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

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Book chapters on the topic "Spatial reasoning"

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Johnson, Dana T. "Nets, Drawings, and Mat Plans." In Spatial Reasoning, 78–91. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-13.

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Johnson, Dana T. "Assessment." In Spatial Reasoning, 6. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-4.

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Johnson, Dana T. "Introduction to Dimensions." In Spatial Reasoning, 17–25. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-8.

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Johnson, Dana T. "Postassessment." In Spatial Reasoning, 105–11. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-15.

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Johnson, Dana T. "Unit Extensions." In Spatial Reasoning, 115. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-17.

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Johnson, Dana T. "Preassessment." In Spatial Reasoning, 10–16. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-7.

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Johnson, Dana T. "Introduction to the Unit." In Spatial Reasoning, 3–5. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-2.

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Johnson, Dana T. "Unit Materials." In Spatial Reasoning, 5–6. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-3.

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Johnson, Dana T. "Polygons and Tangrams." In Spatial Reasoning, 57–69. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-11.

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Johnson, Dana T. "Polyominoes." In Spatial Reasoning, 70–77. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238126-12.

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Conference papers on the topic "Spatial reasoning"

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Wang, Ming Hung, and Jung Hua Chu. "Spatial Delimitation and Spatial Reasoning." In CAADRIA 1997. CAADRIA, 1997. http://dx.doi.org/10.52842/conf.caadria.1997.015.

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Dutta, Soumitra. "Approximate spatial reasoning." In the first international conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/51909.51925.

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Du, Xiaochu, Qingsheng Guo, and Quanfang Wang. "Geographic spatial reasoning strategy based on ontology." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838281.

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Harfmann, Anton C., and Bruce R. Majkowski. "Component-Based Spatial Reasoning." In ACADIA 1992: Computer Supported Design in Architecture: Mission, Method, Madness. ACADIA, 1992. http://dx.doi.org/10.52842/conf.acadia.1992.103.

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Harfmann, Anton C., and Bruce R. Majkowski. "Component-Based Spatial Reasoning." In ACADIA 1992: Computer Supported Design in Architecture: Mission, Method, Madness. ACADIA, 1992. http://dx.doi.org/10.52842/conf.acadia.1992.103.

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Mirzaee, Roshanak, and Parisa Kordjamshidi. "Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning." In Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-emnlp.221.

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Kara, Levent Burak, and Thomas F. Stahovich. "Spatial Reasoning About Mechanical Behaviors." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dtm-21684.

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Abstract We describe an approach that uses causal reasoning and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal behaviors that the feature causes to occur and those it prevents from occurring. The focus of this paper is geometric reasoning techniques that reveal causal relationships between the caused and prevented behaviors. For example, these techniques can determine if a particular caused behavior is responsible for preventing a particular prevented behavior.
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Hadzikadic, Mirsad, and Su-shing Chen. "Spatial Reasoning: Learning from Observations." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, edited by Paul S. Schenker. SPIE, 1990. http://dx.doi.org/10.1117/12.969980.

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Doshi, Rajkumar S., James E. White, Raymond Lam, and David J. Atkinson. "Reasoning With Inaccurate Spatial Knowledge." In Robotics and IECON '87 Conferences, edited by David P. Casasent and Ernest L. Hall. SPIE, 1988. http://dx.doi.org/10.1117/12.942800.

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Dube, Matthew P., and Max J. Egenhofer. "Partitions to improve spatial reasoning." In the 1st ACM SIGSPATIAL PhD Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2694859.2694864.

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Reports on the topic "Spatial reasoning"

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Mulgaonkar, Prasanna. Data Driven Spatial Reasoning. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada242727.

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Forbus, Kenneth D. Qualitative Spatial Reasoning: Framework and Frontiers. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada466021.

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Stone, Gregory. Discussion & analysis: Spatial reasoning assessment. The Learning Partnership, January 2021. http://dx.doi.org/10.51420/report.2021.2.

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A complete Rasch multi-faceted analysis was performed on the draft Spatial Reasoning Assessment. While the psychometric report presents the complete analysis for the examination, this discussion proceeds step-by-step to understand the way in which the analysis proceeded, and the findings therein. The findings were that holistically, the instrument performed admirably. As a pretest, it is likely that students were not expected to demonstrate certain reasoning skills (e.g., GIS) as indeed they did not. The rating scale functions well to capture the examiner judgement. Overall, the instrument works together as a functional assessment, capturing the general construct of Spatial Reasoning.
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David, Uttal, Katherine James, Steven McGee, and Phillip Boda. Laying the Foundation for a Spatial Reasoning Researcher-Practitioner Partnership with CPS, SILC, and The Learning Partnership. Northwestern University, September 2021. http://dx.doi.org/10.51420/report.2020.1.

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The goal of this project was to explore how explicit instruction in spatial reasoning in primary grades can contribute to reductions in variation in STEM outcomes for low-income, minority students in the Chicago Public Schools (CPS). Our project focused on the persistent gender, racial and ethnic, and socioeconomic inequalities in STEM educational and career achievement and attainment. Our approach to addressing this problem was guided by research evidence that much of the variation in STEM outcomes for these groups can be explained by spatial reasoning abilities. Importantly, spatial reasoning skills can be improved through practice, but are rarely explicitly taught in the classroom. The spatial reasoning needs and opportunities identified by this work are relevant to CPS in that they focus on the prevalent science, math, and computer science curricula currently used in CPS K-2 instruction. As such, our findings provide specific, actionable guidance for the development of curricular supports that infuse explicit spatial reasoning instruction.
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Lutz, Carsten. TheComplexity of Reasoning with Concrete Domains (Revised Version). Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.88.

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Description logics are knowledge representation and reasoning formalisms which represent conceptual knowledge on an abstract logical level. Concrete domains are a theoretically well-founded approach to the integration of description logic reasoning with reasoning about concrete objects such as numbers, time intervals or spatial regions. In this paper, the complexity of combined reasoning with description logcis and on concrete domains is investigated. We extend ALC(D), which is the basic description logic for reasoning with concrete domains, by the operators 'feature agreement' and 'feature disagreement'. For the extended logic,called ALCF(D), an algorithm for deciding the ABox consistency problem is devised. The strategy employed by this algorithm is vital for the efficient implementation of reasoners for description logics incorporating concrete domains. Based on the algorithm, it is proved that the standard reasoning problems for both logics ALC(D) and ALCF(D) are PSpace-complete - provided that the satisfiability test of the concrete domain used is in PSpace.
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Xavier, P. G., and R. A. LaFarge. A configuration space toolkit for automated spatial reasoning: Technical results and LDRD project final report. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/453746.

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Lutz, Carsten, and Maja Miličić. A Tableau Algorithm for DLs with Concrete Domains and GCIs. Technische Universität Dresden, 2005. http://dx.doi.org/10.25368/2022.150.

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We identify a general property of concrete domains that is sufficient for proving decidability of DLs equipped with them and GCIs. We show that some useful concrete domains, such as temporal one based on the Allen relations and a spatial one based on the RCC-8 relations, have this property. Then, we present a tableau algorithm for reasoning in DLs equipped with such concrete domains.
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Lutz, Carsten, and Frank Wolter. Modal Logics of Topological Relations. Technische Universität Dresden, 2004. http://dx.doi.org/10.25368/2022.142.

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The eight topological RCC8(or Egenhofer-Franzosa)- relations between spatial regions play a fundamental role in spatial reasoning, spatial and constraint databases, and geographical information systems. In analogy with Halpern and Shoham’s modal logic of time intervals based on the Allen relations, we introduce a family of modal logics equipped with eight modal operators that are interpreted by the RCC8-relations. The semantics is based on region spaces induced by standard topological spaces, in particular the real plane. We investigate the expressive power and computational complexity of the logics obtained in this way. It turns our that, similar to Halpern and Shoham’s logic, the expressive power is rather natural, but the computational behavior is problematic: topological modal logics are usually undecidable and often not even recursively enumerable. This even holds if we restrict ourselves to classes of finite region spaces or to substructures of region spaces induced by topological spaces. We also analyze modal logics based on the set of RCC5relations, with similar results.
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Shastri, Lokendra. Spatio-Temporal Neural Networks for Vision, Reasoning and Rapid Decision Making. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada299746.

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Designing an Escape Room Game to Develop Problem Solving and Spatial Reasoning Skills. Purdue University, August 2018. http://dx.doi.org/10.5703/1288284316855.

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