An Experimental Study of Team Size and Performance on a Complex Task

Odor discrimination is a complex task that may be improved by increasing sampling time to facilitate evidence accumulation. However, experiments testing this phenomenon in olfaction have produced conflicting results. To resolve this disparity, Frederick et al. (Frederick DE, Brown A, Tacopina S, Mehta N, Vujovic M, Brim E, Amina T, Fixsen B, Kay LM. J Neurosci 37: 4416–4426, 2017) conducted experiments that suggest that sampling time and performance are task dependent. Their findings have implications for understanding olfactory processing and experimental design, specifically the effect of subtle differences in experimental design on study results.

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Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

10.36227/techrxiv.16640095 ◽

2021 ◽

Author(s):

Yahia Zakaria ◽

Mayada Hadhoud ◽

Magda Fayek

Keyword(s):

Experimental Study ◽

Deep Learning ◽

Mixture Models ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Generative Models ◽

Generative Adversarial Networks ◽

Sample Quality ◽

Diversity Sampling ◽

And Performance

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

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EXPERIMENTAL STUDY AND PERFORMANCE OF FLAT BELT CONVEYER SYSTEM WITH DIFFERENT SPEED

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v6.i12.2018.1102 ◽

2018 ◽

Vol 6 (12) ◽

pp. 147-150

Author(s):

Aiman Beg ◽

Narendra Jaiswal

Keyword(s):

Experimental Study ◽

Industrial Application ◽

Material Handling ◽

Handling Time ◽

Belt Conveyer ◽

Conveyer System ◽

And Performance ◽

Proper Performance

we are studying about the flat belt conveyer system with different speed, in this way we are many component used for proper performance. The flat belt conveyer system is most important device for reduce the material handling time which is very necessary in industrial application, for this purpose we are construct the highly efficiently flat belt conveyer system using of different distance between two axis of shafts.

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