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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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Torrini, Paolo. "Qualitative spatial reasoning with super-intuitionistic logics." Thesis, University of Leeds, 2003. http://etheses.whiterose.ac.uk/1322/.
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Topology is used in many applications that may benefit from the automation of spatial reasoning, notably in geographic information systems and in graphics. Reasoning about topology is known to be intrinsically complex, and difficult to be dealt with by a machine. Qualitative formalisms for spatial reasoning, region-based approaches to mereotopology, encodings based on non-classical logics are some of the possible answers that have emerged in connection with this problem. The present analysis is based on the well-known topological semantics of intuitionistic logic. That semantics is considered here from the point of view of the representation of spatial knowledge, and accordingly extended, in order to allow more naturally the expression of simple topological descriptions. Special attention is given to the formal modelling of digital representation, to the logical encoding of connectivity relations, to the concepts of granularity and dimension. The formalisations that are investigated are based on some extensions of intuitionistic propositional logic. These can be obtained by adding to the basic logic propositional quantifiers, intuitionistic modalities and intermediate axioms. A proof-checking tool for some of these logics has been developed, by formalising them in Isabelle-HOL, an interactive theorem-prover based on classical higher-order logic. A partial decidability result is given for an extension of intuitionistic second-order propositional logics, together with an account of its mechanisation.
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Hostetter, Michael. "Analogical representation in temporal, spatial, and mnemonic reasoning." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03242009-040545/.
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Lane, Spencer Dale. "Propositional and activity monitoring using qualitative spatial reasoning." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105619.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 77-80).
Communication is the key to effective teamwork regardless of whether the team members are humans or machines. Much of the communication that makes human teams so effective is non-verbal; they are able to recognize the actions that the other team members are performing and take their own actions in order to assist. A robotic team member should be able to make the same inferences, observing the state of the environment and inferring what actions are being taken. In this thesis I introduce a novel approach to the combined problem of activity recognition and propositional monitoring. This approach breaks down the problem into smaller sub-tasks. First, the raw sensor input is parsed into simple, easy to understand primitive semantic relationships known as qualitative spatial relations (QSRs). These primitives are then combined to estimate the state of the world in the same language used by most planners, planning domain definition language (PDDL) propositions. Both the primitives and propositions are combined to infer the status of the actions that the human is taking. I describe an algorithm for solving each of these smaller problems and describe the modeling process for a variety of tasks from an abstracted electronic component assembly (ECA) scenario. I implemented this scenario on a robotic testbed and collected data of a human performing the example actions.
by Spencer Dale Lane.
S.M.
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Yoon, Taehun. "Object Recognition Based on Multi-agent Spatial Reasoning." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1206075792.
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Bennett, Brandon. "Logical representations for automated reasoning about spatial relationships." Thesis, University of Leeds, 1997. http://etheses.whiterose.ac.uk/1271/.
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This thesis investigates logical representations for describing and reasoning about spatial situations. Previously proposed theories of spatial regions are investigated in some detail - especially the 1st-order theory of Randell, Cui and Cohn (1992). The difficulty of achieving effective automated reasoning with these systems is observed. A new approach is presented, based on encoding spatial relations in formulae of 0-order ('propositional') logics. It is proved that entailment, which is valid according to the standard semantics for these logics, is also valid with respect to the spatial interpretation. Consequently, well-known mechanisms for propositional reasoning can be applied to spatial reasoning. Specific encodings of topological relations into both the modal logic S4 and the intuitionistic propositional calculus are given. The complexity of reasoning using the intuitionistic representation is examined and a procedure is presented with is shown to be of O(n3) complexity in the number of relations involved. In order to make this kind of representation sufficiently expressive the concepts of model constraint and entailment constraint are introduced. By means of this distinction a 0-order formula may be used either to assert or to deny that a certain spatial constraint holds of some situation. It is shown how the proof theory of a 0-order logical language can be extended by a simple meta-level generalisation to accommodate a representation involving these two types of formula. A number of other topics are dealt with: a decision procedure based on quantifier elimination is given for a large class of formulae within a 1st-order topological language; reasoning mechanisms based on the composition of spatial relations are studied; the non-topological property of convexity is examined both from the point of view of its 1st-order characterisation and its incorporation into a 0-order spatial logic. It is suggested that 0-order representations could be employed in a similar manner to encode other spatial concepts.
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Dufour-Lussier, Valmi. "Reasoning with qualitative spatial and temporal textual cases." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0182/document.
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Cette thèse propose un modèle permettant la mise en œuvre d'un système de raisonnement à partir de cas capable d'adapter des procédures représentées sous forme de texte en langue naturelle, en réponse à des requêtes d'utilisateurs. Bien que les cas et les solutions soient sous forme textuelle, l'adaptation elle-même est d'abord appliquée à un réseau de contraintes temporelles exprimées à l'aide d'une algèbre qualitative, grâce à l'utilisation d'un opérateur de révision des croyances. Des méthodes de traitement automatique des langues sont utilisées pour acquérir les représentations algébriques des cas ainsi que pour regénérer le texte à partir du résultat de l'adaptationThis thesis proposes a practical model making it possible to implement a case-based reasoning system that adapts processes represented as natural language text in response to user queries. While the cases and the solutions are in textual form, the adaptation itself is performed on networks of temporal constraints expressed with a qualitative algebra, using a belief revision operator. Natural language processing methods are used to acquire case representations and to regenerate text based on the adaptation result
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Sioutis, Michaël. "Algorithmic contributions to qualitative constraint-based spatial and temporal reasoning." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0401/document.
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Le raisonnement spatial et temporel qualitatif est un domaine principal d’études de l’intelligence artificielle et, en particulier, du domaine de la représentation des connaissances, qui traite des concepts cognitifs fondamentaux de l’espace et du temps de manière abstraite. Dans notre thèse, nous nous focalisons sur les formalismes du domaine du raisonnement spatial et temporel qualitatif représentant les informations par des contraintes et apportons des contributions sur plusieurs aspects. En particulier, étant donnée des bases de connaissances d’informations qualitatives sur l’espace ou le temps, nous définissons des nouvelles conditions de consistance locale et des techniques associées afin de résoudre efficacement les problèmes fondamentaux se posant. Nous traitons notamment du problème de la satisfiabilité qui est le problème de décider s’il existe une interprétation quantitative de toutes les entités satisfaisant l’ensemble des contraintes qualitatives. Nous considérons également le problème de l’étiquetage minimal qui consiste à déterminer pour toutes les contraintes qualitatives les relations de base participant à au moins une solution ainsi que le problème de redondance consistant à déterminer les contraintes qualitatives non redondantes. En outre, nous enrichissons le domaine des formalismes spatio-temporels par des contributions concernant une logique spatio-temporelle combinant la logique temporelle propositionnelle (PTL) avec un langage de contraintes qualitatives spatiales et une étude de la problématique consistant à gérer une séquence temporelle de configurations spatiales qualitatives devant satisfaire des contraintes de transitionQualitative Spatial and Temporal Reasoning is a major field of study in Artificial Intelligence and, particularly, in Knowledge Representation, which deals with the fundamental cognitive concepts of space and time in an abstract manner. In our thesis, we focus on qualitative constraint-based spatial and temporal formalisms and make contributions to several aspects. In particular, given a knowledge base of qualitative spatial or temporal information, we define novel local consistency conditions and related techniques to efficiently solve the fundamental reasoning problems that are associated with such knowledge bases. These reasoning problems consist of the satisfiability problem, which is the problem of deciding whether there exists a quantitative interpretation of all the entities of a knowledge base such that all of its qualitative relations are satisfied by that interpretation, the minimal labeling problem, which is the problem of determining all the atoms for each of the qualitative relations of a knowledge base that participate in at least one of its solutions, and the redundancy problem, which is the problem of obtaining all the non-redundant qualitative relations of a knowledge base. Further, we enrich the field of spatio-temporal formalisms that combine space and time in an interrelated manner by making contributions with respect to a qualitative spatio-temporal logic that results by combining the propositional temporal logic (PTL) with a qualitative spatial constraint language, and by investigating the task of ordering a temporal sequence of qualitative spatial configurations to meet certain transition constraints
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Bacon, Alison Margaret. "Individual differences and strategies for human reasoning." Thesis, University of Plymouth, 2003. http://hdl.handle.net/10026.1/349.
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Theories of human reasoning have tended to assume cognitive universality, i.e. that all individuals reason in basically the same way. However, some research (e.g. that of Ford. 1995) has found evidence of individual differences in the strategies people use for syllogistic reasoning. This thesis presents a series of experiments which aimed to identify individual differences in strategies for human reasoning and investigate their nature and aetiology. Experiment 1 successfully replicated and extended Ford (1995) and provided further evidence that most individuals prefer to reason with either verbal-propositional or visuo-spatial representations. Data from verbal and written protocols showed that verbal reasoners tended to use a method of substitution whereby they obtain a value for the common term from one premise and then simply substitute it in the other premise to obtain a conclusion. Spatial reasoners, on the other hand, presented protocols which resembled Euler circles and described the syllogistic premises in terms of sets and subsets. Experiment 2 provided some further qualitative evidence about the nature of such strategies, especially the verbal reasoners, showing that within strategy variations occurred. Experiment 3 extended this line of research, identifying a strong association between verbal and spatial strategies for syllogistic reasoning and abstract and concrete strategies for transitive inference (the latter having originally been identified by Egan and Grimes- Farrow, 1982). Experiments 1-3 also showed that inter-strategic differences in accuracy are generally not observed, hence, reasoners present an outward appearance of ubiquity despite underlying differences in reasoning processes. Experiments 5 and 6 investigated individual differences in cognitive factors which may underpin strategy preference. Whilst no apparent effects of verbal and spatial ability or cognitive style were found, reasoners did appear to draw differentially on the verbal and spatial components of working memory. Confirmatory factor analysis showed that whilst verbal reasoners draw primarily on the verbal memory resource, spatial reasoners draw both on this and on spatial resource. Overall, these findings have important implications for theories of human reasoning, which need to take into account possible individual differences in strategies if they are to present a truly comprehensive account of how people reason.
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Bränd, Stefan. "Using Rigid Landmarks to Infer Inter-Temporal Spatial Relations in Spatio-Temporal Reasoning." Thesis, Linköpings universitet, Artificiell intelligens och integrerad datorsystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-124064.
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Spatio-temporal reasoning is the area of automated reasoning about space and time and is important in the field of robotics. It is desirable for an autonomous robot to have the ability to reason about both time and space. ST0 is a logic that allows for such reasoning by, among other things, defining a formalism used to describe the relationship between spatial regions and a calculus that allows for deducing further information regarding such spatial relations. An extension of ST0 is ST1 that can be used to describe the relationship between spatial entities across time-points (inter-temporal relations) while ST0 is constrained to doing so within a single time-point. This allows for a better ability of expressing how spatial entities change over time. A major obstacle in using ST1 in practise however, is the fact that any observations made regarding spatial relations between regions is constrained to the time-point in which the observation was made, so we are unable to observe inter-temporal relations. Further complicating things is the fact that deducing such inter-temporal relations is not possible without a frame of reference. This thesis examines one method of overcoming these problems by considering the concept of rigid regions which are assumed to always be unchanging and using them as the frame of reference, or as landmarks. The effectiveness of this method is studied by conducting experiments where a comparison is made between various landmark ratios with respect to the total number of regions under consideration. Results show that when a high degree of intra-temporal relations are fully or partially known, increasing the number of landmark regions will reduce the percentage of inter-temporal relations to be completely unknown. Despite this, very few inter-temporal relations can be fully determined even with a high ratio of landmark regions.
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Power, Christopher P. H. "Qualitative reasoning framework for process systems with spatial patterns." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/11121.
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Howarth, Richard J. "Spatial representation, reasoning and control for a surveillance system." Thesis, Queen Mary, University of London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369664.
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Richard, Laurence. "The contribution of non-spatial information to geographic reasoning." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1396966668.
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Capon, Alison Jayne. "Working memory and human reasoning : an individual differences approach." Thesis, University of Plymouth, 2000. http://hdl.handle.net/10026.1/348.
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Experiments 1-3 investigated the relationship between working memory and syllogistic and five-ten-n series spatial inference. A secondary aim was to replicate the findings of Shah and Miyake (1996) who suggested the use of separate central resources of working memory for spatial and verbal ability. The correlational analysis showed that the complex verbal and spatial working memory span tasks were associated together and consistently predicted reasoning performance in both verbal and visual modalities. The confirmatory factor analysis showed that three factors best accounted for the data -a verbal, a spatial, and a general resource. All the span tasks and most of the reasoning tasks significantly and consistently loaded the general factor. Experiments 4-6 investigated the relationship between working memory and a range of reasoning tasks - identified as either propositional. spatial, or quantifiable tasks. These experiments were based on the work of Stanovich and West (1998) who found that a range of reasoning tasks were predicted by cognitive ability and a reasoner's thinking style. The correlational anaylsis showed that the complex verbal and spatial working memory span tasks were associated together and consistently predicted reasoning perforinance. Two clusters of reasoning task emerged from the correlational analysis - one cluster related to the propositional and simple spatial reasoning tasks, whilst the other related to the quantifiable and complex spatial reasoning tasks. The confin-natory factor analysis showed that four factors best accounted for the data -a verbal, a spatial, a general, and a thinking style resource. All the span tasks and the reasoning tasks loaded the general factor, and most of the reasoning tasks further loaded the thinking disposition factor. These results are discussed in light of models of workino memory, theories of reasoning, and how to best characterise factor 3 (executive function) and factor 4 (thinking style) from tile factor analysis.
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Dylla, Frank. "An agent control perspective on qualitative spatial reasoning : towards more intuitive spatial agent development /." [Berlin] : Aka, 2008. http://d-nb.info/989997510/04.
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Roberts, Maxwell. "Individual differences and strategy selection in problem solving." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334796.
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Riva, Mateus. "Spatial Relational Reasoning in Machine Learning : Deep Learning and Graph Clustering." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT043.
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Cette thèse étudie les capacités des méthodes d'apprentissage automatique à raisonner sur des relations spatiales, en particulier sur les relations directionnelles, et l'utilisation de connaissances relationnelles, connues a priori, par ces méthodes. Il existe de nombreux travaux dans le domaine de l'exploitation de connaissances sur les relations dans des méthodes d'apprentissage automatique. Cependant, ce corpus de travaux laisse encore plusieurs questions ouvertes. Tout au long de cette thèse, nous explorons, étudions et tentons d'expliquer différentes questions de recherche liées à ces questions.Nous proposons une amélioration de l'apprentissage des réseaux de neurones convolutionnels (CNN) via une fonction de coût de régularisation intégrant l'information relationnelle. Pour cela, nous proposons deux nouvelles fonctions de coût qui favorisent la satisfaction des relations pendant l'entraînement des CNN, et nous concevons des expériences synthétiques pour montrer leur impact. Alors que les fonctions proposées montrent des améliorations par rapport à un modèle de base non modifié dans des scénarios synthétiques spécifiques et stricts, l'impact sur des scénarios plus généraux est moins significatif. Ce résultat n'est pas facile à expliquer, car l'entrainement des réseaux neuronaux est un processus opaque, et une exploration plus approfondie est nécessaire pour comprendre comment un CNN apprend (ou n'apprend pas) à raisonner en utilisant des informations relationnelles.Pour mieux comprendre comment un CNN peut apprendre à raisonner en utilisant des informations relationnelles, nous proposons un large éventail d'expériences synthétiques. Nous explorons les processus qui permettent, facilitent ou entravent le raisonnement "standard" des CNN sur les relations. Nous proposons une expérience fondamentale pour démontrer qu'un CNN de base, non modifié, est capable de raisonner sur les relations dans certains scénarios. Ensuite, nous explorons quelles relations sont apprises par le CNN, en effectuant l'inférence sur des scènes où les relations a priori sont perturbées, en comparant les résultats, et en entraînant et testant les CNN sur des données synthétiques avec plus ou moins de relations disponibles. Nous étudions ensuite les limites posées au raisonnement relationnel par le champ réceptif du réseau, et approfondissons notre analyse sur des situations où la quantité de données d'entraînement est insuffisante. Enfin, nous examinons à quel moment de la formation les relations sont satisfaites, ce qui permet de comprendre à quel moment les relations elles-mêmes sont apprises.En suivant une approche de groupement de noeuds dans des graphes pour l'utilisation des informations relationnelles, nous utilisons les relations dans un contexte d'apprentissage automatique différent, celui de la découverte de communautés dans des graphes. Nous formulons le groupement dans des graphes comme un problème de correspondance inexacte entre le graphe à analyser et un graphe modèle qui encode les connaissances a priori sur la façon dont les communautés ou les clusters sont liés les uns aux autres. Nous comparons cette approche avec les approches traditionnelles de groupement dans des graphes sur un ensemble de graphes synthétiques, pour mettre en évidence les avantages d'une approche relationnelle, ainsi que sur des graphes réelsThis thesis studies the capabilities of machine learning methods for reasoning on spatial relationships, with a particular focus on directional relationships, and the use of prior relational information by these methods. There are many works in the field of applying knowledge on relationships to machine learning methods. However, this body of work still leaves several open questions. Throughout this thesis, we explore, investigate and attempt to explain different research questions linked to this field.We propose an improvement to the training of CNNs via a regularisation loss function based on relational information. To this end, we propose two novel loss functions which reward relationship satisfaction during CNN training, and design synthetic experiments to showcase their impact. While the proposed loss functions show improvements over an unmodified baseline in specific, strict synthetic scenarios, the impact on more ``generic'' training scenarios is less significant. This result is not easily explainable, as neural network training is a significantly opaque process, and as such, a deeper exploration is required to understand how a CNN learns (or fails to learn) to reason using relational information.To further understanding of how a CNN can learn to reason using relational information, we propose a wide array of distinct synthetic experiments. We explore the processes which enable, facilitate, or hinder ``standard'' CNN reasoning on relationships. We propose a fundamental experience to demonstrate that a basic, unmodified CNN is capable of relational reasoning in some scenarios. Next, we explore which relationships are learned by the CNN, by performing inference on scenes where the prior relationships are disturbed, by recording the difference in the results, and by training and testing CNNs on synthetic data with more or less relationships available. We then investigate the limits placed on relational reasoning by the network receptive field, as well as deepen our analysis on situations where the amount of training data is insufficient. Finally, we explore at which moment during training relationships are satisfied, as a proxy for understanding at which moment the relationships themselves are learned.Following a graph-clustering approach to the usage of relational information, we explore prior relationships in a different machine learning context, that of community discovery on graphs. We formulate graph clustering as an inexact matching problem between the graph to be clustered and a model graph which encodes prior knowledge on how the communities or clusters relate to each other. We compare this approach with traditional graph clustering approaches on a set of synthetic graphs, to showcase the advantages of a relational-aware approach, as well as on real graphs
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Ho, Chun-Heng. "Spatial Cognition in Design." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14150.
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Previous studies suggest that 3D visualization is fundamental to design spatial cognition, and the capability to work with 3D mental or physical models and taking perspective views from a set of 2D drawings are essential parts of design education, although there is no definitive evidence that can directly support these beliefs. This dissertation focuses on the issues of how spatial capabilities correlate with design performance and whether design education can improve students' spatial capabilities. Two types of capabilities tests, i.e. spatial capability test and general reasoning test, are used, and there are 251 Georgia Tech undergraduate students involved in this research. The results of this research suggest that the correlations between design studio performance and the tested factors are more salient among female students than male students. While female students generally have lower spatial capabilities than male students in design, they can take advantage of their general reasoning capability to compensate for the lack of the other two spatial capabilities and perform well in design studio. A stepwise regression further reveals that, for the female design student group, the general reasoning capability is the only predictor for their design performance. However, no significant interaction is observed in the male design student group between tested capabilities and design studio performance. As a result, there seems to exist a threshold requirement in spatial capabilities for design major students. After passing this threshold, other factors such as domain specific skills and knowledge or self-motivation would likely to be the dominant one. Lastly, although the results show the tested capabilities are all important for design major students from different perspectives, the design education does not show any contribution in the improvement of these capabilities.
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Perkins, Eric David 1975. "Spatial reasoning for generalized N-body physics : discrete element algorithms." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80180.
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Blum, Anthony John. "An investigation into the psychology of spatial and temporal reasoning /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487841548269909.
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Forti, Maicol. "Logic Reasoning in BDI Agents: Current Trends and Spatial Integrations." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23426/.
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This thesis finds its place in the context of BDI agents and aims to enable a form of situated spatial reasoning.
A survey is proposed in which the possible techniques and technologies that can be integrated into the BDI model to provide a form of spatial reasoning are analyzed.
This review highlights a technological gap that we have therefore decided to fill, with the goal of providing a way to locate logical information in certain spatial areas and to be able to constrain reasoning on them.
In this thesis we propose Geo2p, a technological prototype based on 2P-Kt that allows you to query situated information in tuProlog, enabling a form of spatial reasoning: given a region of space where certain Clauses are valid, a Theory can be defined, constraining the knowledge on what is true in the selected area.
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Wallgrün, Jan Oliver. "Hierarchical Voronoi graphs spatial representation and reasoning for mobile robots." Berlin Heidelberg Springer, 2008. http://d-nb.info/99728210X/04.
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Calazans, Campelo Claudio Elizio. "Representing and reasoning about changing spatial extensions of geographic features." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/5843/.
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This thesis presents a novel approach to representing and reasoning about geographic phenomena which can be interpreted based on changes affecting spatial extensions of geographic features. Of particular interest in this work are geographic features whose extensions can be described as 2-dimensional regions corresponding to portions of the earth surface under a specified projection, such as deserts, forests and oceans. The work resulted in the development of a logical framework for representing geographic events and processes. In developing such a framework, issues have been addressed regarding the relationship between these concepts and also between them and geographic features. Other crucial issues are how to define the relation between event and process types and their particular instances, and how to handle different kinds of vagueness to associate specific spatial and temporal boundaries with those instances. Of particular interest in this work is the development of a method of explicitly linking the formalism to spatio-temporal data. This requires work at multiple levels, both in consideration of how the data can be represented and in regards of how primitive elements of the logical framework can be defined. Although data can be regarded as a faithful reproduction of physical elements of the world, some conceptual elements are not always explicitly represented within data. For that reason, a logic-based approach to representing spatio-temporal geographic data was also developed and is presented in this thesis. Representing the data in a logical fashion allows implicit data to be derived by means of logical inferences, and provides a natural way of explicitly connecting the data to a semantic-based formalism. Derived data may include spatial extensions of geographic features at different times, based on existing data describing, for example, portions of the earth’s surface associated with different observable properties. Furthermore, a system has been implemented to evaluate the applicability of the proposed theory. The system takes time-stamped topographic data as an input and allows logical queries to be formulated about the data, returning textual and graphical information on geographic events, processes, and features which participate in them.
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Varn, Theresa. "EFFECTS OF A MATHEMATICS CURRICULUM RICH IN SPATIAL REASONING ACTIVITIES ON FIFTH GRADE STUDENTS' ABILITIES TO SPATIALLY REASON:." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2129.
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ABSTRACT The purpose of this study is to describe the effect of a curriculum rich in spatial reasoning activities and experiences on the ability of my fifth grade students to spatially reason. The study was conducted to examine 1) the effects of my practice of incorporating spatial reasoning lessons and activities in my fifth-grade mathematics classroom on the students' ability to spatially reason and 2) the effects of my practice of incorporating spatial reasoning lessons and activities on my students' ability to problem solve. Data were collected over a ten-week period through the use of student interviews, anecdotal records, photos of student work, student journals, pre- and posttests and a post-study survey. In this study, students demonstrated a statistically significant increase on all pre- and posttests. The student interviews, anecdotal records, photos of student work, and student journals all revealed spatial reasoning was used in mathematics problem solving. The study suggests that spatial reasoning can be taught and spatial reasoning skills can be used in problem solving.M.A.
Department of Teaching and Learning Principles
Education
K-8 Mathematics and Science Education
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Schultz, Carl 1984. "Methodologies for the development of qualitative spatial and temporal reasoning applications." Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/6040.
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A great variety of scientific, engineering-based, and commercial application domains are fundamentally grounded in concepts of space and time. Over the last three decades there has been significant interest in more human-focused and intuitive qualitative spatial and temporal reasoning (QSTR) methods, which address the inherent limitations of purely numerical approaches for reasoning about space and time. However, despite the extremely significant theoretical advances that have been made in the QSTR field, there is a distinct absence of commercial and industrial applications that utilise QSTR calculi. The central issue is that relatively little research has addressed the unique challenges of designing and developing QSTR-based applications in comparison to more traditional systems that employ numerical processing techniques. The primary objective of this thesis is to support software engineering practitioners in the development of applications that utilise QSTR calculi. Five QSTR application case studies, which cover a range of diverse application domains, are presented and analysed throughout this thesis to motivate the development of effective methodologies. Furthermore, a comprehensive definition of QSTR applications is presented to provide a formal basis for establishing methodologies that address three major areas of QSTR application development: requirements specification, design, and validation. Design methodologies are presented that enable developers to evaluate the efficacy of numerous QSTR calculi with respect to QSTR application functional requirements. Additionally, the design methodologies adapt object-oriented concepts and machine learning techniques to facilitate the development of custom, high level, application-specific qualitative relations and constraints. Four key validation methodologies are adapted from well known techniques in software engineering: unit testing, integration testing, test coverage, and mutation testing. Furthermore, a novel metric called H-complexity is presented and used to define four additional test coverage classes that a developer can employ to assess the efficacy of a test suite. Finally, a meta-validation methodology is established that enables developers and the QSTR community to empirically investigate the efficacy of QSTR validation techniques. Experiments are conducted using the meta-validation methodology and the results are analysed to identify the most effective utilisation of QSTR validation methodologies according to the software development process being employed.
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Sharma, Jayant. "Integrated Spatial Reasoning in Geographic Information Systems: Combining Topology and Direction." Fogler Library, University of Maine, 1996. http://www.library.umaine.edu/theses/pdf/Sharma.pdf.
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Wolter, Diedrich. "Spatial representation and reasoning for robot mapping a shape-based approach." Berlin Heidelberg Springer, 2006. http://d-nb.info/989966941/34.
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Abdelmoty, El-Hetw Alia Ibrahim. "Modelling and reasoning in spatial databases : a deductive object-oriented approach." Thesis, Heriot-Watt University, 1995. http://hdl.handle.net/10399/615.
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Wolter, Diedrich. "Spatial representation and reasoning for robot mapping a shape-based approach /." Berlin : Springer, 2008. http://www.myilibrary.com?id=186085.
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Jiang, Bo. "Formal Reasoning and Spatial Ability: A Step towards "Science for All"." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002651.
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Bailey, David Thomas. "Development of an optimal spatial decision-making system using approximate reasoning." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16202/1/David_Bailey_Thesis.pdf.
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There is a recognised need for the continued improvement of both the techniques and technology for spatial decision support in infrastructure site selection. Many authors have noted that current methodologies are inadequate for real-world site selection decisions carried out by heterogeneous groups of decision-makers under uncertainty. Nevertheless despite numerous limitations inherent in current spatial problem solving methods, spatial decision support systems have been proven to increase decision-maker effectiveness when used. However, due to the real or perceived difficulty of using these systems few applications are actually in use to support decision-makers in siting decisions. The most common difficulties encountered involve standardising criterion ratings, and communicating results. This research has focused on the use of Approximate Reasoning to improve the techniques and technology of spatial decision support, and make them easier to use and understand. The algorithm developed in this research (ARAISS) is based on the use of natural language to describe problem variables such as suitability, certainty, risk and consensus. The algorithm uses a method based on type II fuzzy sets to represent problem variables. ARAISS was subsequently incorporated into a new Spatial Decision Support System (InfraPlanner) and validated by use in a real-world site selection problem at Australia's Brisbane Airport. Results indicate that Approximate Reasoning is a promising method for spatial infrastructure planning decisions. Natural language inputs and outputs, combined with an easily understandable multiple decision-maker framework created an environment conducive to information sharing and consensus building among parties. Future research should focus on the use of Genetic Algorithms and other Artificial Intelligence techniques to broaden the scope of existing work.
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Bailey, David Thomas. "Development of an optimal spatial decision-making system using approximate reasoning." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16202/.
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There is a recognised need for the continued improvement of both the techniques and technology for spatial decision support in infrastructure site selection. Many authors have noted that current methodologies are inadequate for real-world site selection decisions carried out by heterogeneous groups of decision-makers under uncertainty. Nevertheless despite numerous limitations inherent in current spatial problem solving methods, spatial decision support systems have been proven to increase decision-maker effectiveness when used. However, due to the real or perceived difficulty of using these systems few applications are actually in use to support decision-makers in siting decisions. The most common difficulties encountered involve standardising criterion ratings, and communicating results. This research has focused on the use of Approximate Reasoning to improve the techniques and technology of spatial decision support, and make them easier to use and understand. The algorithm developed in this research (ARAISS) is based on the use of natural language to describe problem variables such as suitability, certainty, risk and consensus. The algorithm uses a method based on type II fuzzy sets to represent problem variables. ARAISS was subsequently incorporated into a new Spatial Decision Support System (InfraPlanner) and validated by use in a real-world site selection problem at Australia's Brisbane Airport. Results indicate that Approximate Reasoning is a promising method for spatial infrastructure planning decisions. Natural language inputs and outputs, combined with an easily understandable multiple decision-maker framework created an environment conducive to information sharing and consensus building among parties. Future research should focus on the use of Genetic Algorithms and other Artificial Intelligence techniques to broaden the scope of existing work.
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Frazee, Leah M. "The Interaction of Geometric and Spatial Reasoning: Student Learning of 2D Isometries in a Special Dynamic Geometry Environment." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531862080144028.
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Flanagan, Gregory M. "Conceptual Requirement Validation for Architecture Design Systems." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/653.
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Computer-aided architectural design (CAAD) programs represent architectural design at a low level of spatial abstraction. While this representation model allows CAAD programs to capture the precise spatial characteristics of a design, it means that CAAD programs lack the underlying computational apparatus necessary to reason about design at a conceptual level.
This thesis is a first step towards building a framework that bridges the gap between the conceptual aspects of a design and its low level CAAD-based spatial representation. Specifically, this thesis presents a new framework, referred to as the Conceptual Requirements Reasoner (CRR), which provides an architect with a framework to validate conceptual design requirements. The CRR will demonstrate how qualitative spatial representation and reasoning techniques can be used as the link between a design's conceptual requirements and its underlying quantitative spatial representation.
A museum case study is presented to demonstrate the application of the CRR in a real world design context. It introduces a set of museum design requirements identified in research and shows how these requirements can be validated using the CRR. The results of the case study shows that the CRR is an effective tool for conceptual requirements reasoning.
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Liu, Jiming. "Qualitative spatial reasoning with applications to planar mechanism analysis and motion planning." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=41676.
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This thesis presents a framework for qualitatively representing and reasoning about spatial configurations in the two-dimensional case. The framework contains interval/region based formalisms for describing distances, angles, and locations. These representations lead to the formulation of inference rules, as well as search-heuristics which enable the derivation of spatial relationships and paths among objects. In order to generate approximate quantitative configurations, local search strategies such as simulated annealing are applied within the configuration space deduced from the qualitative spatial description.The qualitative framework can be used in two principal ways: first, to formulate solutions to spatial configuration problems where exact geometric knowledge is not available, and second, to provide guidance for the application of quantitative configuration analysis and planning methods. These have direct relevance to, and implications for, computer-aided mechanism design and robotics. In this thesis, two applications of this framework are demonstrated.
The first application is concerned with inferring the instantaneous configurations, coupler curves, and relative velocities in one-degree-of-freedom planar linkages, given their qualitative link lengths. Based on the qualitative framework, algorithms have been developed and implemented to tackle each aspect of the linkage problem. The simulation results show that the coupler curves derived from the proposed algorithms can correctly capture the significant topological and geometric properties of the actual curves.
The second application deals with planning collision-free paths for open-chain planar mechanisms moving among static obstacles. Using the qualitative framework, a quantitative path can be composed by generating and connecting a sequence of path segments based on a global route found in the qualitative spatial planning. This approach to path planning is illustrated in two case studies; one involves a single mobile robot whereas the other considers a serially coupled manipulator.
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Vázquez, Marynel. "Reasoning About Spatial Patterns of Human Behavior During Group Conversations with Robots." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1062.
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The goal of this dissertation is to develop computational models for robots to detect and sustain the spatial patterns of behavior that naturally emerge during free-standing group conversations with people. These capabilities have often been overlooked by the Human-Robot Interaction (HRI) community, but they are essential for robots to appropriately interact with and around people in many human environments. In this work, we first develop a robotic platform for studying human-robot interactions, and contribute new experimental protocols to investigate group conversations with robots. The studies that we conducted with these protocols examine various aspects of these interactions, and experimentally validate the idea that people tend to establish spatial formations typical of human conversations with robots. These formations emerge as the members of the interaction cooperate to sustain a single focus of attention. They maximize their opportunities for monitoring one another’s mutual perceptions during conversations. Second, we introduce a general framework to track the lower-body orientation of freestanding people in human environments and to detect their conversational groups based on their spatial behavior. This framework takes advantage of the mutual dependency between the two problems. Lower-body orientation is a key descriptor of spatial behavior and, thus, can help detect group conversations. Meanwhile, knowing the location of group conversations can help estimate people’s lower-body orientation, because these interactions often bias human spatial behavior. We evaluate this framework in a public computer vision benchmark for group detection, and show how it can be used to estimate the members of a robot’s group conversation in real-time. Third, we study how robots should orient with respect to a group conversation to cooperate to sustain the spatial arrangements typical of these interactions. To this end, we conduct an experiment to study the effects of varied orientation and gaze behaviors for robots during social conversations. Our results reinforce the importance of communicative motion behavior for robots, and suggest that their body and gaze behaviors should be designed and controlled jointly, rather than independently of each other. We then show in simulation that it is possible to use reinforcement learning techniques to generate socially appropriate orientation behavior for robots during group conversations. These techniques reduce the amount of engineering required to enable robots to sustain spatial formations typical of conversations while communicating attentiveness to the focus of attention of the interaction. Overall, our efforts show that reasoning about spatial patterns of behavior is useful for robots. This reasoning can help with perception tasks as well as generating appropriate robot behavior during social group conversations.
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Kreutzmann, Arne [Verfasser], Christian [Akademischer Betreuer] Freksa, and Alessandro [Akademischer Betreuer] Saffiotti. "Qualitative Spatial and Temporal Reasoning based on And/Or Linear Programming : an approach to partially grounded qualitative spatial reasoning / Arne Kreutzmann. Gutachter: Christian Freksa ; Alessandro Saffiotti. Betreuer: Christian Freksa." Bremen : Staats- und Universitätsbibliothek Bremen, 2014. http://d-nb.info/1072226669/34.
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Graham, Charlotte. "The relationship between inhibitory control and System 1 and System 2 processes in deductive and spatial reasoning." Thesis, University of Canterbury. Psychology, 2007. http://hdl.handle.net/10092/1370.
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Dual Processing theory proposes that the ability to over ride associative (System 1) in favour of analytical (System 2) processed in deductive reasoning may depend on inhibitory control. The present study applies this association to a spatial reasoning task by adapting a mental rotation task to a multichoice format including System 1 (mirror) and System 2 (rotated image) responses. Fifty undergraduate volunteers from the University of Canterbury responded to a Stroop task as a measure of inhibitory control that was compared with System 1 and System 2 responding from a spatial and a deductive reasoning task. It was expected that people with weaker inhibitory potential would make more System 1 and fewer System 2 responses in both deductive and visual-spatial reasoning tasks. Contrary to expectation System 2 responding dominated for both tasks and correlations between both reasoning tasks and measures of inhibitory control were non-significant. The differing idiosyncratic demands of each task may have obscured any common variables associated with inhibitory control. This research initiated a test for the presence of System 1 and System 2 in spatial reasoning.
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Wolter, Diedrich [Verfasser]. "Spatial representation and reasoning for robot mapping : a shape-based approach / Diedrich Wolter." Berlin, 2008. http://d-nb.info/989966941/34.
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Zimmerman, Ellen L. "2D and 3D Fabrication Devices: Can They Improve Spatial Reasoning Skills in Children?" Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862838/.
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The purpose of this study is to evaluate the potential benefit of two hours of activities involving 2D and 3D fabricators on the spatial reasoning skills of children in Grades 4 and 5, ages 9 to 10, from a private school in Southeast Texas. Can the introduction to hands-on activities with products created with these devices and learning about how these devices operate improve spatial reasoning skills? The research also evaluates the use of the Shapes Test as a valid measure of the spatial reasoning skills of children. The Cube Design and Spatial Memory subtests of the UNIT (Universal Nonverbal Intelligence Tests) were used for evaluating the spatial reasoning skills of the participants, based on their respected validity, along with a Shapes Test that is in development. Discussion regarding gender, language, and experiential theories of spatial reasoning skill development are included in the literature review.
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Cole, Merryn L. "SPATIAL REASONING AND UNDERSTANDING THE PARTICULATE NATURE OF MATTER: A MIDDLE SCHOOL PERSPECTIVE." UKnowledge, 2017. http://uknowledge.uky.edu/edsc_etds/26.
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This dissertation employed a mixed-methods approach to examine the relationship between spatial reasoning ability and understanding of chemistry content for both middle school students and their science teachers. Spatial reasoning has been linked to success in learning STEM subjects (Wai, Lubinski, & Benbow, 2009). Previous studies have shown a correlation between understanding of chemistry content and spatial reasoning ability (e.g., Pribyl & Bodner, 1987; Wu & Shah, 2003: Stieff, 2013), raising the importance of developing the spatial reasoning ability of both teachers and students.
Few studies examine middle school students’ or in-service middle school teachers’ understanding of chemistry concepts or its relation to spatial reasoning ability. The first paper in this dissertation addresses the quantitative relationship between mental rotation, a type of spatial reasoning ability, and understanding a fundamental concept in chemistry, the particulate nature of matter. The data showed a significant, positive correlation between scores on the Purdue Spatial Visualization Test of Rotations (PSVT; Bodner & Guay, 1997) and the Particulate Nature of Matter Assessment (ParNoMA; Yezierski, 2003) for middle school students prior to and after chemistry instruction. A significant difference in spatial ability among students choosing different answer choices on ParNoMA questions was also found. The second paper examined the ways in which students of different spatial abilities talked about matter and chemicals differently.
Students with higher spatial ability tended to provide more of an explanation, though not necessarily in an articulate matter. In contrast, lower spatial ability students tended to use any keywords that seemed relevant, but provided little or no explanation. The third paper examined the relationship between mental reasoning and understanding chemistry for middle school science teachers. Similar to their students, a significant, positive correlation between scores on the PSVT and the ParNoMA was observed. Teachers who used consistent reasoning in providing definitions and examples for matter and chemistry tended to have higher spatial abilities than those teachers who used inconsistent reasoning on the same questions. This is the first study to explore the relationship between spatial reasoning and understanding of chemistry concepts at the middle school level. Though we are unable to infer cause and effect relationship from correlational data, these results illustrate a need to further investigate this relationship as well as identify the relationship between different spatial abilities (not just mental rotation) and other chemistry concepts.