Task planning and scheduling are crucial for construction or fabrication (CF) processes. Automating them is necessary for more efficient plans in terms of time and resources. However, most construction planning proces...
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Task planning and scheduling are crucial for construction or fabrication (CF) processes. Automating them is necessary for more efficient plans in terms of time and resources. However, most construction planning processes are still performed manually despite the existence of various AI methods. Symbolic AI automated task planning (ATP) techniques offer a variety of features to tackle task planning problems, but their application to CF has not been researched yet. This study identifies the current state of research and gaps in the literature regarding these AI techniques while providing directions for future research. We conduct a systematic review that evaluates existing literature on ATP in terms of environmental characteristics, modeling languages, ATP techniques, and results. We searched the ACM, IEEE, Scopus, WOS, and SpringerLink databases for papers published in the last 20 years (2002-2022) that discuss symbolic AI methods used in task planning within the CF fields. Our findings indicate that research on automated planning is currently limited regarding the characteristics of CF environments. Only a few papers have utilized symbolic languages, AI planners, and ATP techniques. No paper has evaluated their planning system in an on-site CF process. As a result, many symbolic languages, planners, and ATP techniques remain unexplored.
The specific topics covered include: quasi-conformal and quasi-isometric mappings, hyperelastic deformations, multidimensional generalisations of the equidistribution principle, discrete differential geometry, spatial...
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ISBN:
(数字)9783030234362
ISBN:
(纸本)9783030234355;9783030234386
The specific topics covered include: quasi-conformal and quasi-isometric mappings, hyperelastic deformations, multidimensional generalisations of the equidistribution principle, discrete differential geometry, spatial and metric encodings, Voronoi-Delaunay theory for tilings and partitions, duality in mathematical programming and numerical geometry, mesh-based optimisation and optimal control methods. Further aspects examined include iterative solvers for variational problems and algorithm and software development. The applications of the methods discussed are multidisciplinary and include problems from mathematics, physics, biology, chemistry, material science, and engineering.
This book constitutes the refereed proceedings of the 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, held in Beijing, China in June 2013. The 45 high-quality papers presented were careful...
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ISBN:
(数字)9783642387869
ISBN:
(纸本)9783642387852
This book constitutes the refereed proceedings of the 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, held in Beijing, China in June 2013. The 45 high-quality papers presented were carefully reviewed and selected from 68 submissions. BICS 2013 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of brain inspired cognitive systems research and applications in diverse fields.
In terms of content, the book strikes a balance between engineering algorithms and mathematical foundations. It presents an overview of recent advances in numerical geometry, grid generation and adaptation in ter...
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ISBN:
(数字)9783030767983
ISBN:
(纸本)9783030767976;9783030768003
In terms of content, the book strikes a balance between engineering algorithms and mathematical foundations. It presents an overview of recent advances in numerical geometry, grid generation and adaptation in terms of mathematical foundations, algorithm and software development and applications.
Sparse matrix-vector semiring computation is a key operation in sparse matrix computations, with performance strongly dependent on both program design and the features of the sparse matrices. Given the diversity of sp...
Sparse matrix-vector semiring computation is a key operation in sparse matrix computations, with performance strongly dependent on both program design and the features of the sparse matrices. Given the diversity of sparse matrices, designing a tailored program for each matrix is challenging. To address this, we propose SRSparse1 an program generator that creates tailored programs by automatically combining program designing methods to fit specific input matrices. It provides two components: the problem definition configuration, which declares the computation, and the scheduling language, which can be leveraged by an auto-tuner to specify the program designs. The two are lowered to the intermediate representations of SRSparse, the Format IR and Kernel IR, which respectively generates format conversion routine and kernel code. We evaluate SRSparse on four representative sparse kernels and three format conversion routines. For sparse kernels, SRSparse achieves median speedups over handwritten programs: COO (3.50 ×), CSR-Adaptive (5.36 ×), CSR5 (2.06 ×), ELL (1.63 ×), Gunrock (1.57 ×), and GraphBLAST (1.96 ×); over an auto-tuner: AlphaSparse (1.16 ×); and over a compiler: TACO (1.71 ×). For format conversion routines, SRSparse achieves median speedups over handwritten implementations: Intel MKL (7.60 ×), SPARSKIT (2.61 ×), CUSP (2.77 ×), and Ginkgo (1.74 ×); and over a compiler: TACO (4.04 ×).
This paper introduces a measure of certainty, the characteristic of the similarity of the mathematical model to the actual plant, based on the basic concepts of information theory. The properties of the newly introduc...
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