A Hybrid Approach to Closeness in the Framework of Order of Magnitude Qualitative Reasoning

We introduce the syntax, semantics, and an axiom system for a PDL-based extension of the logic for order of magnitude qualitative reasoning, developed in order to deal with the concept of qualitative velocity, which together with qualitative distance and orientation, are important notions in order to represent spatial reasoning for moving objects, such as robots. The main advantages of using a PDL-based approach are, on the one hand, all the well-known advantages of using logic in AI, and, on the other hand, the possibility of constructing complex relations from simpler ones, the flexibility for using different levels of granularity, its possible extension by adding other spatial components, and the use of a language close to programming languages.

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Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab806 ◽

2021 ◽

Vol 504 (1) ◽

pp. 372-392

Author(s):

Robert W Bickley ◽

Connor Bottrell ◽

Maan H Hani ◽

Sara L Ellison ◽

Hossen Teimoorinia ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Formation Rate ◽

Hybrid Approach ◽

Environmental Parameters ◽

Decision Threshold ◽

Trade Offs ◽

Training Samples ◽

Wide Range ◽

Order Of Magnitude

ABSTRACT The Canada–France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ∼5000 deg2 of the sky, representing a first-rate opportunity to identify recently merged galaxies. Because of the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 per cent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ∼6 per cent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini–M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

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Closeness and Distance Relations in Order of Magnitude Qualitative Reasoning via PDL

Current Topics in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-14264-2_8 ◽

2010 ◽

pp. 71-80 ◽

Cited By ~ 1

Author(s):

Alfredo Burrieza ◽

Emilio Muñoz-Velasco ◽

Manuel Ojeda-Aciego

Keyword(s):

Qualitative Reasoning ◽

Order Of Magnitude

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Synthesis of Initial Form for Structural Shape Optimization

Journal of Vibration and Acoustics ◽

10.1115/1.3269569 ◽

1988 ◽

Vol 110 (4) ◽

pp. 564-570 ◽

Cited By ~ 5

Author(s):

J. J. Shah

Keyword(s):

Hybrid Approach ◽

Qualitative Reasoning ◽

Structural Configuration ◽

Structural Synthesis ◽

Systematic Method ◽

Structural Shape ◽

Initial Form ◽

Structural Shape Optimization ◽

Form Design ◽

Configuration Synthesis

Techniques that use qualitative reasoning for structural configuration synthesis and form design are critically examined. Both algorithmic and heuristic methods are considered. Because of the limitations of these methods a hybrid approach is presented. A shape algebra is developed for structural synthesis which leads to a systematic method for generating structural arrangements. This algebra raises the possibility of transforming structural synthesis into a science instead of the art that it currently is. The method is well-suited to automation exploiting a computer’s capability to manipulate symbols. The method searches for good candidate structures that can be used as the initial form for numerical programs for shape and size optimization.

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