aaai-19_促进AI研究：在AAAI-19与我们会面

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Last month, several members from the AI @ Unity team were present at NeurIPS in Montreal. At the Unity booth, we had the opportunity to meet hundreds of researchers and introduce them to Artificial Intelligence and Machine Learning projects at Unity. Later this month, we’re heading to AAAI-19 (an annual AI conference) in Honolulu where we’ll be hosting a booth, and also co-organizing the AAAI-19 Workshop on Games and Simulations for Artificial Intelligence. In this blog post, we’ll provide you with a brief overview of the workshop and explain why we are eager to foster research that leverages games and simulation platforms.

上个月，AI @ Unity团队的几位成员出席了在蒙特利尔的NeurIPS 。 在Unity展位上，我们有机会让数百名研究人员见面，并向他们介绍了Unity的人工智能和机器学习项目。 本月晚些时候，我们将前往火奴鲁鲁的AAAI-19 (年度AI会议)，我们将在此举办一个展位，并共同组织AAAI-19 人工智能游戏和模拟研讨会 。 在此博客文章中，我们将为您提供研讨会的简要概述，并解释为什么我们渴望促进利用游戏和模拟平台的研究。

If you’re attending AAAI, consider joining our workshop on January 28 – it’s packed with fantastic speakers and papers covering games and simulations for AI. Also, drop by our booth (January 29 – 31) to say hi, watch some demos, and learn about teams and projects at Unity.

如果您要参加AAAI，请考虑参加我们于1月28日举行的讲习班，其中包含了出色的演讲者和论文，涵盖了AI游戏和模拟。 另外，请在1月29日至31日的展位前下来打个招呼，观看一些演示，并了解Unity的团队和项目。

人工智能研究中的游戏简史 (A brief history of games in AI research)

Games have a long history in AI research, dating back to at least 1949 when Claude Shannon (shortly after developing information entropy) got interested in writing a computer program to play the game of Chess. In his paper “Programming a Computer for Playing Chess”, Shannon writes:

游戏在AI研究中有着悠久的历史，至少可以追溯到1949年，当时克劳德·香农(Claude Shannon)(在发展信息熵之后不久)对编写计算机程序玩象棋游戏感兴趣。 香农在他的论文“为下象棋编程计算机”中写道：

“The chess machine is an ideal one to start with, since: (1) the problem is sharply defined both in allowed operations (the moves) and in the ultimate goal (checkmate); (2) it is neither so simple as to be trivial nor too difficult for satisfactory solution; (3) chess is generally considered to require “thinking” for skilful[sic] play; a solution of this problem will force us either to admit the possibility of a mechanized thinking or to further restrict our concept of “thinking”; (4) the discrete structure of chess fits well into the digital nature of modern computers.”

“象棋机是一台理想的棋盘机，因为：(1)在允许的操作(移动)和最终目标(将死)中都明确定义了问题； (2)对于满意的解决方案来说，它既不那么简单，也不是太困难； (3)国际象棋通常被认为需要技巧才能熟练地玩； 解决该问题将迫使我们要么接受机械化思考的可能性，要么进一步限制我们的“思考”概念； (4)国际象棋的离散结构非常适合现代计算机的数字本质。”

That was in 1949. Since then, there has been an enduring interest in creating computer programs that can play games as skillfully as human players, even beating respective world champions. Shannon inspired Arthur Samuel’s seminal work on Checkers in the 1950’s and 1960’s. While Samuel’s program was unable to beat expert players, it was considered a major achievement as it was the first program to effectively utilize heuristic search procedures and learning-based methods. The first success story of achieving expert-level ability was Chinook, a checkers program developed at the University of Alberta in 1989 that began beating most human players and by 1994 the best players could at best play to a draw. This trend continued with other 2-player board games such as Backgammon (with Gerald Tesauro’s TD-Gammon, 1992-2002) and Chess (when IBM’s Deep Blue beat Garry Kasparov, 1997), and most recently with Go. An important scientific breakthrough of the last few years was when, in 2016, DeepMind’s AlphaGo beat 18-time world champion Lee Sedol 4 to 1, the subject of the Netflix documentary, AlphaGo.

那是在1949年。从那时起，人们一直对创建能够像人类玩家一样熟练地玩游戏，甚至击败各自的世界冠军的计算机程序产生了浓厚的兴趣。 香农在1950年代和1960年代启发了亚瑟·塞缪尔(Arthur Samuel)关于Checkers的开创性工作。 尽管Samuel的程序无法击败专家玩家，但它被认为是一项重大成就，因为它是第一个有效利用启发式搜索程序和基于学习方法的程序。 达到专家级水平的第一个成功故事是奇努克(Chinook)，这是一项由艾伯塔大学于1989年开发的跳棋程序，该程序开始击败大多数人类选手，到1994年，最优秀的选手充其量可以发挥最大的吸引力。 这种趋势在其他2人棋盘游戏中继续存在，例如西洋双陆棋(步步高(与Gerald Tesauro的TD-Gammon，1992-2002年)和国际象棋(当IBM的Deep Blue击败Garry Kasparov，1997年)，最近与Go一起。 过去几年中的一项重要的科学突破是，2016年DeepMind的AlphaGo以18比4击败了18届世界冠军Lee Sedol，这是Netflix纪录片AlphaGo的主题。

(Source) Chinook vs Marion Tinsley (1994)

( 源 )大鳞VS马里恩廷斯利(1994)

 The progress over the last 70 years since Claude Shannon’s paper has not been limited to solving increasingly more difficult 2-player board games but has expanded to other complex scenarios. These include 3D multiplayer games such as Starcraft II and Dota 2 and more challenging game tasks such as learning to play Doom and Atari 2600 games using only the raw screen pixel inputs instead of a hand-coded representation of the game state. In a 2015 Nature paper, DeepMind presented a deep reinforcement learning system, termed deep Q-network (DQN), that was able to achieve superhuman performance on a number of Atari 2600 games using only the raw screen pixel inputs. What was particularly remarkable was how a single system (fixed input/output spaces, algorithm, and parameters), trained independently on each game, was able to perform well on such a large number of diverse games. More recently, OpenAI developed OpenAI Five, a team of five neural networks that can compete with amateur players in Dota 2.

自从克劳德·香农(Claude Shannon)发表论文以来的70年来，进展不仅限于解决越来越困难的2人棋盘游戏，还扩展到了其他复杂的场景。 这些包括3D多人游戏，例如Starcraft II和Dota 2，以及更具挑战性的游戏任务，例如仅使用原始屏幕像素输入而不是游戏状态的手动编码来学习玩Doom和Atari 2600游戏。 DeepMind在2015年的Nature论文中提出了一种称为深度Q网络(DQN)的深度强化学习系统，该系统能够仅使用原始屏幕像素输入就可以在许多Atari 2600游戏中实现超人的性能。 尤其引人注目的是，在每个游戏上独立训练的单个系统(固定的输入/输出空间，算法和参数)如何能够在如此众多的多样化游戏上表现良好。 最近，OpenAI开发了OpenAI Five ，这是一个由五个神经网络组成的团队，可以与Dota 2中的业余玩家竞争。

游戏引擎和模拟平台的有效性 (The effectiveness of game engines & simulation platforms)

It’s not just games that have played a central role in AI development. Game engines (and other simulation platforms) themselves are now becoming a powerful tool for researchers across many disciplines such as robotics, computer vision, autonomous vehicles, and natural language understanding.

在AI开发中发挥核心作用的不仅仅是游戏。 现在，游戏引擎(和其他模拟平台)本身已成为跨许多领域的研究人员的强大工具，例如机器人技术，计算机视觉，自动驾驶汽车和自然语言理解。

A primary reason for adopting game engines for AI research is the ability to generate large amounts of synthetic data. This is exceptionally powerful as recent advances in AI and the availability of managed hardware in the cloud (e.g. GPUs, TPUs) have resulted in algorithms that can efficiently leverage huge volumes of data. Our partnership with DeepMind is one example of a premier research lab fully investing in utilizing virtual worlds to study AI. The use of game engines is even more profound in scenarios in which data set generation in the real world is prohibitively expensive or dangerous. A second reason for adopting game engines is their rendering quality and physics fidelity which enables the study of real-world problems in a safe and controlled environment. It also enables models trained on synthetic data to be transferred to the real world with minimal changes. A common example is training self-driving cars and Baidu’s move to leverage Unity to evaluate its algorithms is representative of an ongoing shift to embrace modern game engines.

采用游戏引擎进行AI研究的主要原因是能够生成大量合成数据。 由于AI的最新发展以及云中托管硬件(例如GPU， TPU )的可用性，导致算法可以有效利用大量数据，因此，此功能异常强大。 我们与DeepMind的合作伙伴关系就是一流的研究实验室的一个例子，该实验室已全力投资利用虚拟世界来研究AI。 在现实世界中生成数据集的成本过高或危险的情况下，游戏引擎的使用更为广泛。 采用游戏引擎的第二个原因是它们的渲染质量和物理保真度，这使得能够在安全可控的环境中研究现实问题。 它也使经过综合数据训练的模型可以以最小的更改传递到现实世界。 一个常见的例子是对自动驾驶汽车进行培训，而百度利用Unity来评估其算法的举动代表了正在拥抱现代游戏引擎的持续转变。

AI is dubbed the new electricity due to its potential to transform multiple industries. We foresee game engines and simulation platforms playing a very important role in that transformation. This is evident by the large number of platforms that have recently been created to study a number of research problems such as playing video games, physics-based control, locomotion, 3D pose estimation, natural language instruction following, embodied question answering, and autonomous vehicles (e.g. Arcade Learning Environment, Starcraft II Learning Environment, ViZDoom, General Video Game AI, MuJoCo, Gibson, Allen Institute AI2-Thor, Facebook House3D, Microsoft AirSim, CARLA). The list also includes our own Unity ML-Agents Toolkit which can be used to transform any Unity scene into a learning environment to train intelligent agents using deep reinforcement learning and imitation learning algorithms. Consequently, we’re eager to encourage and foster AI research that leverages games and simulation platforms.

人工智能被称为新电力，因为它具有改变多个行业的潜力。 我们预计游戏引擎和模拟平台将在这一转变中扮演非常重要的角色。 最近创建的用于研究许多研究问题的大量平台就可以证明这一点，这些问题包括玩视频游戏，基于物理学的控制，运动，3D姿势估计，自然语言指令跟随，具体问题解答和自动驾驶汽车(例如， 街机学习环境 ， Starcraft II学习环境 ， ViZDoom ， 通用视频游戏AI ， MuJoCo ， 吉布森 ， 艾伦学院AI2-Thor ， Facebook House3D ， Microsoft AirSim ， CARLA )。 该列表还包括我们自己的Unity ML-Agents工具包 ，可用于将任何Unity场景转换为学习环境，以使用深度强化学习和模仿学习算法来训练智能代理。 因此，我们渴望鼓励和促进利用游戏和模拟平台的AI研究。

AAAI-19研讨会概述 (AAAI-19 workshop overview)

At AAAI, later this month, we are co-organizing the Workshop in Games and Simulations for AI with Julian Togelius (Professor at New York University) and Roozbeh Mottaghi (Research Scientist at the Allen Institute for Artificial Intelligence). The workshop will include a full day of presentations by invited speakers and authors of peer-reviewed papers. The presentations will cover a number of topics including large-scale training of deep reinforcement learning systems such as AlphaGo, high-performance rendering for learning robot dexterity, learning to map natural language to controls of a quadcopter, and using drones to protect wildlife in the African savannah. If you are attending AAAI, join us at the workshop to learn more about how games and simulations are being used to power AI research.

在本月晚些时候的AAAI上，我们将与纽约大学教授Julian Togelius和艾伦人工智能研究所的研究科学家Roozbeh Mottaghi共同组织AI游戏和模拟研讨会 。 研讨会将包括由受邀演讲者和同行评审论文的作者作一整天的演讲。 演讲将涵盖多个主题，包括深度强化学习系统(例如AlphaGo)的大规模培训，用于学习机器人灵巧性的高性能渲染，学习将自然语言映射到四轴飞行器的控件以及使用无人机保护无人机中的野生生物。非洲大草原。 如果您正在参加AAAI，请加入我们的研讨会，以了解有关如何使用游戏和模拟来推动AI研究的更多信息。

Aloha!

阿罗哈！

翻译自: https://blogs.unity3d.com/2019/01/18/fostering-ai-research-meet-us-at-aaai-19/

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