The Experts below are selected from a list of 7680 Experts worldwide ranked by ideXlab platform
K. Dill - One of the best experts on this subject based on the ideXlab platform.
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How qualitative Spatial Reasoning can improve strategy game AIs
IEEE Intelligent Systems, 2002Co-Authors: K.d. Forbus, J.v. Mahoney, K. DillAbstract:Spatial Reasoning is a major challenge for strategy-game artificial intelligence systems. Qualitative Spatial Reasoning techniques can help overcome this challenge by providing more expressive Spatial representations, better communication of intent, better path-finding and reusable strategy libraries.
Carl Schultz - One of the best experts on this subject based on the ideXlab platform.
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SUM - Probabilistic Spatial Reasoning in Constraint Logic Programming
Lecture Notes in Computer Science, 2016Co-Authors: Carl Schultz, Mehul Bhatt, Jakob SuchanAbstract:In this paper we present a novel framework and full implementation of probabilistic Spatial Reasoning within a Logic Programming context. The crux of our approach is extending Probabilistic Logic Programming (based on distribution semantics) to support Reasoning over Spatial variables via Constraint Logic Programming. Spatial Reasoning is formulated as a numerical optimisation problem, and we implement our approach within ProbLog 1. We demonstrate a range of powerful features beyond what is currently provided by existing probabilistic and Spatial Reasoning tools.
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Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories
Theory and Practice of Logic Programming, 2016Co-Authors: Przemyslaw Andrzej Walega, Carl Schultz, Mehul BhattAbstract:The systematic modelling of dynamic Spatial systems is a key requirement in a wide range of application areas such as commonsense cognitive robotics, computer-aided architecture design, and dynamic geographic information systems. We present Answer Set Programming Modulo Theories (ASPMT)(QS), a novel approach and fully implemented prototype for non-monotonic Spatial Reasoning — a crucial requirement within dynamic Spatial systems — based on ASPMT. ASPMT(QS) consists of a (qualitative) Spatial representation module (QS) and a method for turning tight ASPMT instances into Satisfiability Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default Spatial Reasoning and Spatial frame axioms. Spatial Reasoning is performed by encoding Spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real non-linear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other contemporary Spatial Reasoning systems both within and outside the context of logic programming. ASPMT(QS) is currently the only existing system that is capable of Reasoning about indirect Spatial effects (i.e., addressing the ramification problem), and integrating geometric and QS information within a non-monotonic Spatial Reasoning context.
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RuleML - A Numerical Optimisation Based Characterisation of Spatial Reasoning
Rule Technologies. Research Tools and Applications, 2016Co-Authors: Carl Schultz, Mehul BhattAbstract:We present a novel numerical optimisation based characterisation of Spatial Reasoning in the context of constraint logic programming (CLP). The approach —formalised and implemented within CLP— is developed as an extension to CLP(QS), a declarative Spatial Reasoning framework providing a range of mixed quantitative-qualitative Spatial representation and Reasoning capabilities. We demonstrate the manner in which the numerical optimisation based extensions further enhance the declarative Spatial Reasoning capabilities of CLP(QS).
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Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories
arXiv: Artificial Intelligence, 2016Co-Authors: Przemyslaw Andrzej Walega, Carl Schultz, Mehul BhattAbstract:The systematic modelling of dynamic Spatial systems is a key requirement in a wide range of application areas such as commonsense cognitive robotics, computer-aided architecture design, and dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monotonic Spatial Reasoning -a crucial requirement within dynamic Spatial systems- based on Answer Set Programming Modulo Theories (ASPMT). ASPMT(QS) consists of a (qualitative) Spatial representation module (QS) and a method for turning tight ASPMT instances into Satisfiability Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default Spatial Reasoning and Spatial frame axioms. Spatial Reasoning is performed by encoding Spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other contemporary Spatial Reasoning systems both within and outside the context of logic programming. ASPMT(QS) is currently the only existing system that is capable of Reasoning about indirect Spatial effects (i.e., addressing the ramification problem), and integrating geometric and qualitative Spatial information within a non-monotonic Spatial Reasoning context. This paper is under consideration for publication in TPLP.
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aspmt qs non monotonic Spatial Reasoning with answer set programming modulo theories
International Conference on Logic Programming, 2015Co-Authors: Przemyslaw Andrzej Walega, Mehul Bhatt, Carl SchultzAbstract:The systematic modelling of dynamic Spatial systems [9] is a key requirement in a wide range of application areas such as comonsense cognitive robotics, computer-aided architecture design, dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monotonic Spatial Reasoning —a crucial requirement within dynamic Spatial systems– based on Answer Set Programming Modulo Theories (ASPMT). ASPMT(QS) consists of a (qualitative) Spatial representation module (QS) and a method for turning tight ASPMT instances into Sat Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default Spatial Reasoning and Spatial frame axioms using choice formulas. Spatial Reasoning is performed by encoding Spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other prominent contemporary Spatial Reasoning systems. Our results show that ASPMT(QS) is the only existing system that is capable of Reasoning about indirect Spatial effects (i.e. addressing the ramification problem), and integrating geometric and qualitative Spatial information within a non-monotonic Spatial Reasoning context.
K.d. Forbus - One of the best experts on this subject based on the ideXlab platform.
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How qualitative Spatial Reasoning can improve strategy game AIs
IEEE Intelligent Systems, 2002Co-Authors: K.d. Forbus, J.v. Mahoney, K. DillAbstract:Spatial Reasoning is a major challenge for strategy-game artificial intelligence systems. Qualitative Spatial Reasoning techniques can help overcome this challenge by providing more expressive Spatial representations, better communication of intent, better path-finding and reusable strategy libraries.
Michael Mitchelmore - One of the best experts on this subject based on the ideXlab platform.
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Towards a framework for Spatial Reasoning and primary mathematics learning: an analytical synthesis of intervention studies
Mathematics Education Research Journal, 2020Co-Authors: Geoffrey Woolcott, Brent Davis, Joanne Mulligan, Thi Tran, Michael MitchelmoreAbstract:The connection between Spatial Reasoning and mathematics learning and pedagogy in primary school children has been the subject of an increasing number of studies in recent years. There has been no comprehensive analysis, however, of how studies based on Spatial Reasoning interventions may lead to improvements in students’ mathematics learning in school classroom environments. This article considers 18 studies selected from a combined systematic literature review of 133 studies, from Scopus and Education Research Complete (ERC) using PRISMA, and 23 studies recommended by the research team from bibliographies of major international research centres with a Spatial Reasoning dedication. This combination approach has allowed a synthesis of research and practice in an analytical way, assisting construction of a framework for Spatial Reasoning interventions for consideration in developing core knowledge and skills within the primary school mathematics curriculum. The findings highlight the importance of designing and evaluating Spatial Reasoning programs for primary school children in order to improve students’ mathematics classroom learning, including evidence from standardized tests, as they progress through the school system. The article supports the need for further research on interventions that provide sustainable school-based Spatial Reasoning programs.
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Connecting mathematics learning through Spatial Reasoning
Mathematics Education Research Journal, 2017Co-Authors: Joanne Mulligan, Geoffrey Woolcott, Michael Mitchelmore, Brent DavisAbstract:Spatial Reasoning, an emerging transdisciplinary area of interest to mathematics education research, is proving integral to all human learning. It is particularly critical to science, technology, engineering and mathematics (STEM) fields. This project will create an innovative knowledge framework based on Spatial Reasoning that identifies new pathways for mathematics learning, pedagogy and curriculum. Novel analytical tools will map the unknown complex systems linking Spatial and mathematical concepts. It will involve the design, implementation and evaluation of a Spatial Reasoning Mathematics Program (SRMP) in Grades 3 to 5. Benefits will be seen through development of critical Spatial skills for students, increased teacher capability and informed policy and curriculum across STEM education.
Geoffrey Woolcott - One of the best experts on this subject based on the ideXlab platform.
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Towards a framework for Spatial Reasoning and primary mathematics learning: an analytical synthesis of intervention studies
Mathematics Education Research Journal, 2020Co-Authors: Geoffrey Woolcott, Brent Davis, Joanne Mulligan, Thi Tran, Michael MitchelmoreAbstract:The connection between Spatial Reasoning and mathematics learning and pedagogy in primary school children has been the subject of an increasing number of studies in recent years. There has been no comprehensive analysis, however, of how studies based on Spatial Reasoning interventions may lead to improvements in students’ mathematics learning in school classroom environments. This article considers 18 studies selected from a combined systematic literature review of 133 studies, from Scopus and Education Research Complete (ERC) using PRISMA, and 23 studies recommended by the research team from bibliographies of major international research centres with a Spatial Reasoning dedication. This combination approach has allowed a synthesis of research and practice in an analytical way, assisting construction of a framework for Spatial Reasoning interventions for consideration in developing core knowledge and skills within the primary school mathematics curriculum. The findings highlight the importance of designing and evaluating Spatial Reasoning programs for primary school children in order to improve students’ mathematics classroom learning, including evidence from standardized tests, as they progress through the school system. The article supports the need for further research on interventions that provide sustainable school-based Spatial Reasoning programs.
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The Re-emergence of Spatial Reasoning Within Primary Years Mathematics Education
Research in Mathematics Education in Australasia 2016–2019, 2020Co-Authors: Geoffrey Woolcott, Tracy Logan, Margaret Marshman, Ajay Ramful, Robert Whannell, Thomas LowrieAbstract:This chapter presents a review of the re-emergence of Spatial Reasoning in Australasia as a potentially powerful but under-utilised bridging mechanism between real-world experiences and mathematics teaching and learning. This is the first time a chapter has been dedicated solely to Spatial Reasoning in the Mathematics Education Research Group of Australasia’s (MERGA’s) four yearly review and hence the chapter outlines preliminary studies that have formed the basis for the research profiled in the 2016–2019 period. The focus on primary years (Foundation to Year six) mathematics reflects a resurgence of insights from the 1980s amplified as a research focus on the interaction of Spatial Reasoning and mathematics development during childhood. Because mathematical concept formation is connected to interaction with the three-dimensional world in both a mathematical and non-mathematical way it will be important to Spatialise the primary curriculum. The review includes coverage of the work of established Australasian research projects, along with smaller studies and literature emanating from intervention programs that are not nominally Spatial, but have Spatial underpinnings or Spatial Reasoning components. While further research is needed to explore teacher knowledge and practice, this chapter acknowledges the valuable contributions and global influence of re-emerging Australasian research.
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Connecting mathematics learning through Spatial Reasoning
Mathematics Education Research Journal, 2017Co-Authors: Joanne Mulligan, Geoffrey Woolcott, Michael Mitchelmore, Brent DavisAbstract:Spatial Reasoning, an emerging transdisciplinary area of interest to mathematics education research, is proving integral to all human learning. It is particularly critical to science, technology, engineering and mathematics (STEM) fields. This project will create an innovative knowledge framework based on Spatial Reasoning that identifies new pathways for mathematics learning, pedagogy and curriculum. Novel analytical tools will map the unknown complex systems linking Spatial and mathematical concepts. It will involve the design, implementation and evaluation of a Spatial Reasoning Mathematics Program (SRMP) in Grades 3 to 5. Benefits will be seen through development of critical Spatial skills for students, increased teacher capability and informed policy and curriculum across STEM education.
