Organising an AI challenge does not end with the final event. The
long-lasting impact also needs to be organised. This chapter covers the various
activities after the challenge is formally finished. The target audience of
different post-challenge activities is identified. The various outputs of the
challenge are listed with the means to collect them. The main part of the
chapter is a template for a typical post-challenge paper, including possible
graphs as well as advice on how to turn the challenge into a long-lasting
benchmark.

AI 挑战的后续活动包括识别不同类型的活动的目标受众、列举挑战的各种输出及其收集方法，并提供了一个典型后续活动论文的模板，包括可能的图表以及如何将挑战转化为一个长期有效的基准的建议。

人工智能竞赛与基准测试：如何通过后续论文、基准测试和其他传播行动确保挑战的长期影响

AI Competitions and Benchmarks: How to ensure a long-lasting impact of a  challenge with post-challenge paper, benchmarks and other dissemination  action

The scientific community is able to present a new set of solutions to
practical problems that substantially improve the performance of modern
technology in terms of efficiency and speed of computation due to the
advancement in neural networks architectures. We present the latest works
considering the utilization of neural networks in robot path planning. Our
survey shows the contrast between different formulations of the problems that
consider different inputs, outputs, and environments and how different neural
networks architectures are able to provide solutions to all of the presented
problems.

利用神经网络在机器人路径规划中的应用有不同的输入、输出和环境，不同的网络架构能够提供解决方案。

神经网络路径规划

Neural Networks for Path Planning

This abstract extends on the previous work (arXiv:1407.2646,
arXiv:1606.00075) on program induction using probabilistic programming. It
describes possible further steps to extend that work, such that, ultimately,
automatic probabilistic program synthesis can generalise over any reasonable
set of inputs and outputs, in particular in regard to text, image and video
data.

通过概率编程对程序归纳的扩展，实现自动概率编程综合，可以推广应用于任何合理的输入和输出，特别是文本、图像和视频数据。