When Are We Done with Games?
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Iaj 10-3 (2019)
Vol. 10 No. 3 2019 Arthur D. Simons Center for Interagency Cooperation, Fort Leavenworth, Kansas FEATURES | 1 About The Simons Center The Arthur D. Simons Center for Interagency Cooperation is a major program of the Command and General Staff College Foundation, Inc. The Simons Center is committed to the development of military leaders with interagency operational skills and an interagency body of knowledge that facilitates broader and more effective cooperation and policy implementation. About the CGSC Foundation The Command and General Staff College Foundation, Inc., was established on December 28, 2005 as a tax-exempt, non-profit educational foundation that provides resources and support to the U.S. Army Command and General Staff College in the development of tomorrow’s military leaders. The CGSC Foundation helps to advance the profession of military art and science by promoting the welfare and enhancing the prestigious educational programs of the CGSC. The CGSC Foundation supports the College’s many areas of focus by providing financial and research support for major programs such as the Simons Center, symposia, conferences, and lectures, as well as funding and organizing community outreach activities that help connect the American public to their Army. All Simons Center works are published by the “CGSC Foundation Press.” The CGSC Foundation is an equal opportunity provider. InterAgency Journal FEATURES Vol. 10, No. 3 (2019) 4 In the beginning... Special Report by Robert Ulin Arthur D. Simons Center for Interagency Cooperation 7 Military Neuro-Interventions: The Lewis and Clark Center Solving the Right Problems for Ethical Outcomes 100 Stimson Ave., Suite 1149 Shannon E. -
Machine Learning Explained
Learning Deep or not Deep… De MENACE à AlphaGo JM Alliot Le Deep Learning en Version Simple Qu’est-ce que c’est ? Croisement entre deux univers • L’apprentissage • Par renforcement: on est capable d’évaluer le résultat et de l’utiliser pour améliorer le programme • Supervisé: une base de données d’apprentissage est disponible cas par cas • Non supervisé: algorithmes de classification (k- means,…),… • Les neurosciences DLVS L’apprentissage par renforcement • Turing: • « Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child brain is something like a notebook as one buys it from the stationers. Rather little mechanism, and lots of blank sheets.” • “The use of punishments and rewards can at best be a part of the teaching process. Roughly speaking, if the teacher has no other means of communicating to the pupil, the amount of information which can reach him does not exceed the total number of rewards and punishments applied. By the time a child has learnt to repeat “Casabianca” he would probably feel very sore indeed, if the text could only be discovered by a “Twenty Questions” technique, every “NO” taking the form of a blow.” DLVS L’apprentissage par renforcement • Arthur Samuel (1959): « Some Studies in Machine Learning Using the Game of Checkers » • Memoization • Ajustement automatique de paramètres par régression. • Donald Michie (1962): MENACE -
Hearing on China's Military Reforms and Modernization: Implications for the United States Hearing Before the U.S.-China Economic
HEARING ON CHINA'S MILITARY REFORMS AND MODERNIZATION: IMPLICATIONS FOR THE UNITED STATES HEARING BEFORE THE U.S.-CHINA ECONOMIC AND SECURITY REVIEW COMMISSION ONE HUNDRED FIFTEENTH CONGRESS SECOND SESSION THURSDAY, FEBRUARY 15, 2018 Printed for use of the United States-China Economic and Security Review Commission Available via the World Wide Web: www.uscc.gov UNITED STATES-CHINA ECONOMIC AND SECURITY REVIEW COMMISSION WASHINGTON: 2018 U.S.-CHINA ECONOMIC AND SECURITY REVIEW COMMISSION ROBIN CLEVELAND, CHAIRMAN CAROLYN BARTHOLOMEW, VICE CHAIRMAN Commissioners: HON. CARTE P. GOODWIN HON. JAMES TALENT DR. GLENN HUBBARD DR. KATHERINE C. TOBIN HON. DENNIS C. SHEA MICHAEL R. WESSEL HON. JONATHAN N. STIVERS DR. LARRY M. WORTZEL The Commission was created on October 30, 2000 by the Floyd D. Spence National Defense Authorization Act for 2001 § 1238, Public Law No. 106-398, 114 STAT. 1654A-334 (2000) (codified at 22 U.S.C. § 7002 (2001), as amended by the Treasury and General Government Appropriations Act for 2002 § 645 (regarding employment status of staff) & § 648 (regarding changing annual report due date from March to June), Public Law No. 107-67, 115 STAT. 514 (Nov. 12, 2001); as amended by Division P of the “Consolidated Appropriations Resolution, 2003,” Pub L. No. 108-7 (Feb. 20, 2003) (regarding Commission name change, terms of Commissioners, and responsibilities of the Commission); as amended by Public Law No. 109- 108 (H.R. 2862) (Nov. 22, 2005) (regarding responsibilities of Commission and applicability of FACA); as amended by Division J of the “Consolidated Appropriations Act, 2008,” Public Law Nol. 110-161 (December 26, 2007) (regarding responsibilities of the Commission, and changing the Annual Report due date from June to December); as amended by the Carl Levin and Howard P. -
Autonomous Aircraft Sequencing and Separation with Hierarchical Deep Reinforcement Learning
ICRAT 2018 Autonomous Aircraft Sequencing and Separation with Hierarchical Deep Reinforcement Learning Marc Brittain Peng Wei Aerospace Engineering Department Aerospace Engineering Department Iowa State University Iowa State University Ames, IA, USA Ames, IA, USA [email protected] [email protected] Abstract—With the increasing air traffic density and complexity autonomy or human operator decisions, and cope with in traditional controlled airspace, and the envisioned large envisioned high-density and on-demand air traffic by providing volume vertical takeoff and landing (VTOL) operations in low- automated sequencing and separation advisories. altitude airspace for personal air mobility or on-demand air taxi, an autonomous air traffic control system (a fully automated The inspiration of this paper is twofold. First, the authors airspace) is needed as the ultimate solution to handle dense, were amazed by the fact that an artificial intelligence agent complex and dynamic air traffic in the future. In this work, we called AlphaGo built by DeepMind defeated the world design and build an artificial intelligence (AI) agent to perform champion Ke Jie in three matches of Go in May 2017 [11]. air traffic control sequencing and separation. The approach is to This notable advance in AI field demonstrated the theoretical formulate this problem as a reinforcement learning model and foundation and computational capability to potentially augment solve it using the hierarchical deep reinforcement learning and facilitate human tasks with intelligent agents and AI algorithms. For demonstration, the NASA Sector 33 app has technologies. Therefore, the authors want to apply the most been used as our simulator and learning environment for the recent AI frameworks and algorithms to re-visit the agent. -
Chinese Health App Arrives Access to a Large Population Used to Sharing Data Could Give Icarbonx an Edge Over Rivals
NEWS IN FOCUS ASTROPHYSICS Legendary CHEMISTRY Deceptive spice POLITICS Scientists spy ECOLOGY New Zealand Arecibo telescope faces molecule offers cautionary chance to green UK plans to kill off all uncertain future p.143 tale p.144 after Brexit p.145 invasive predators p.148 ICARBONX Jun Wang, founder of digital biotechnology firm iCarbonX, showcases the Meum app that will use reams of health data to provide customized medical advice. BIOTECHNOLOGY Chinese health app arrives Access to a large population used to sharing data could give iCarbonX an edge over rivals. BY DAVID CYRANOSKI, SHENZHEN medical advice directly to consumers through another $400 million had been invested in the an app. alliance members, but he declined to name the ne of China’s most intriguing biotech- The announcement was a long-anticipated source. Wang also demonstrated the smart- nology companies has fleshed out an debut for iCarbonX, which Wang founded phone app, called Meum after the Latin for earlier quixotic promise to use artificial in October 2015 shortly after he left his lead- ‘my’, that customers would use to enter data Ointelligence (AI) to revolutionize health care. ership position at China’s genomics pow- and receive advice. The Shenzhen firm iCarbonX has formed erhouse, BGI, also in Shenzhen. The firm As well as Google, IBM and various smaller an ambitious alliance with seven technology has now raised more than US$600 million in companies, such as Arivale of Seattle, Wash- companies from around the world that special- investment — this contrasts with the tens of ington, are working on similar technology. But ize in gathering different types of health-care millions that most of its rivals are thought Wang says that the iCarbonX alliance will be data, said the company’s founder, Jun Wang, to have invested (although several big play- able to collect data more cheaply and quickly. -
Fml-Based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
Accepted for publication in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems in July, 2017 FML-BASED DYNAMIC ASSESSMENT AGENT FOR HUMAN-MACHINE COOPERATIVE SYSTEM ON GAME OF GO CHANG-SHING LEE* MEI-HUI WANG, SHENG-CHI YANG Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan *[email protected], [email protected], [email protected] PI-HSIA HUNG, SU-WEI LIN Department of Education, National University of Tainan, Tainan, Taiwan [email protected], [email protected] NAN SHUO, NAOYUKI KUBOTA Dept. of System Design, Tokyo Metropolitan University, Japan [email protected], [email protected] CHUN-HSUN CHOU, PING-CHIANG CHOU Haifong Weiqi Academy, Taiwan [email protected], [email protected] CHIA-HSIU KAO Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan [email protected] Received (received date) Revised (revised date) Accepted (accepted date) In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML- based Dynamic Assessment Agent (FDAA), and we present an FML-based Human–Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook’s Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. -
NTT Technical Review, Vol. 15, No. 11, Nov. 2017
NTT Technical Review November 2017 Vol. 15 No. 11 Front-line Researchers Naonori Ueda, NTT Fellow, Head of Ueda Research Laboratory and Director of Machine Learning and Data Science Center, NTT Communication Science Laboratories Feature Articles: Communication Science that Enables corevo®—Artificial Intelligence that Gets Closer to People Basic Research in the Era of Artificial Intelligence, Internet of Things, and Big Data—New Research Design through the Convergence of Science and Engineering Generative Personal Assistance with Audio and Visual Examples Efficient Algorithm for Enumerating All Solutions to an Exact Cover Problem Memory-efficient Word Embedding Vectors Synthesizing Ghost-free Stereoscopic Images for Viewers without 3D Glasses Personalizing Your Speech Interface with Context Adaptive Deep Neural Networks Regular Articles Optimization of Harvest Time in Microalgae Cultivation Using an Image Processing Algorithm for Color Restoration Global Standardization Activities Report on First Meeting of ITU-T TSAG (Telecommunication Standardization Advisory Group) for the Study Period 2017 to 2020 Information Event Report: NTT Communication Science Laboratories Open House 2017 Short Reports Arkadin Brings Businesses into the Future with New Cloud Unified Communications Services and Digital Operations Strategies World’s Largest Transmission Capacity with Standard Diameter Multi-core Optical Fiber—Accelerated Multi-core Fiber Application Using Current Standard Technology External Awards/Papers Published in Technical Journals -
Spy on Me #2 – Artistic Manoevres for the Digital Present
Spy on Me Artistic Manoeuvres for the Digital Present #2 19. –29.3.2020 Spy on Me #2 – Artistic Manoeuvres for the Digital Present 19.–29.3.2020 / HAU1, HAU2, HAU3 We have arrived in the reality of digital transformation. Life with screens, apps and algorithms influences our behaviour, our atten - tion and desires. Meanwhile the idea of the public space and of democracy is being reorganized by digital means. The festival “Spy on Me” goes into its second round after 2018, searching for manoeuvres for the digital present together with Berlin-based and international artists. Performances, interactive spatial installati - ons and discursive events examine the complex effects of the digi - tal transformation of society. In theatre, where live encounters are the focus, we come close to the intermediate spaces of digital life, searching for ways out of feeling powerless and overwhelmed, as currently experienced by many users of internet-based technolo - gies. For it is not just in some future digital utopia, but here, in the midst of this present, that we have to deal with the basic conditi - ons of living together as a society and of planetary survival. Are we at the edge of a great digital darkness or at the decisive tur - ning point of perspective? A Festival by HAU Hebbel am Ufer. Funded by: Hauptstadtkulturfonds. What is our rela - tionship with alien conscious - nesses? As we build rivals to human intelligence, James Bridle looks at our relationship with the planet’s other alien consciousnesses. 5 On 27 June 1835, two masters of the ancient DeepBlue defeated Garry Kasparov at chess, prise, strangeness and even horror that Chinese game of Go faced off in a match up to that point a game with Go-like status as AlphaGo evokes will become a feature of more which was the culmination of a years-long a bastion of human imagination and mental and more areas of our lives. -
Computer Wins Second GAME Against Chinese GO Champion
TECHNOLOGY SATURDAY, MAY 27, 2017 Samsung investigating Galaxy S8 ‘iris hack’ SEOUL: Samsung Electronics is investigating claims by a German hacking group that it fooled the iris recognition system of the new flagship Galaxy S8 device, the firm said yesterday. The launch of the Galaxy S8 was a key step for the world’s largest smartphone maker as it sought to move on from last year’s humiliating withdrawal of the fire-prone Galaxy Note 7s, which hammered the firm’s once-stellar reputation. But a video posted by the Chaos Computer Club (CCC), a German hacking group founded in 1981, shows the Galaxy S8 being unlocked using a printed photo of the owner’s eye covered with a contact lens to replicate the curvature of a real eyeball. “A high- resolution picture from the internet is sufficient to cap- ture an iris,” CCC spokesman Dirk Engling said, adding: “Ironically, we got the best results with laser printers made by Samsung.” A Samsung spokeswoman said it was aware of the report and was investigating. The iris scanning technolo- gy was “developed through rigorous testing”, the firm said in a statement as it sought to reassure customers. “If there is a potential vulnerability or the advent of a new method that challenges our efforts to ensure security at WUZHEN: Chinese Go player Ke Jie, left, looks at the board as a person makes a move on behalf of Google’s artificial any time, we will respond as quickly as possible to resolve intelligence program, AlphaGo, during a game of Go at the Future of Go Summit in Wuzhen in eastern China’s Zhejiang the issue.” Samsung’s hopes of competing against archri- Province. -
Global Artificial Intelligence Industry Whitepaper
Global artificial intelligence industry whitepaper Global artificial intelligence industry whitepaper | 4. AI reshapes every industry 1. New trends of AI innovation and integration 5 1.1 AI is growing fully commercialized 5 1.2 AI has entered an era of machine learning 6 1.3 Market investment returns to reason 9 1.4 Cities become the main battleground for AI innovation, integration and application 14 1.5 AI supporting technologies are advancing 24 1.6 Growing support from top-level policies 26 1.7 Over USD 6 trillion global AI market 33 1.8 Large number of AI companies located in the Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta 35 2. Development of AI technologies 45 2.1 Increasingly sophisticated AI technologies 45 2.2 Steady progress of open AI platform establishment 47 2.3 Human vs. machine 51 3. China’s position in global AI sector 60 3.1 China has larger volumes of data and more diversified environment for using data 61 3.2 China is in the highest demand on chip in the world yet relying heavily on imported high-end chips 62 3.3 Chinese robot companies are growing fast with greater efforts in developing key parts and technologies domestically 63 3.4 The U.S. has solid strengths in AI’s underlying technology while China is better in speech recognition technology 63 3.5 China is catching up in application 64 02 Global artificial intelligence industry whitepaper | 4. AI reshapes every industry 4. AI reshapes every industry 68 4.1 Financial industry: AI enhances the business efficiency of financial businesses -
Reinforcement Learning and Video Games Arxiv:1909.04751V1 [Cs.LG]
University of Sheffield Reinforcement Learning and Video Games Yue Zheng Supervisor: Prof. Eleni Vasilaki A report submitted in partial fulfilment of the requirements for the degree of MSc Data Analytics in Computer Science in the arXiv:1909.04751v1 [cs.LG] 10 Sep 2019 Department of Computer Science September 12, 2019 i Declaration All sentences or passages quoted in this document from other people's work have been specif- ically acknowledged by clear cross-referencing to author, work and page(s). Any illustrations that are not the work of the author of this report have been used with the explicit permis- sion of the originator and are specifically acknowledged. I understand that failure to do this amounts to plagiarism and will be considered grounds for failure. Name: Yue Zheng Abstract Reinforcement learning has exceeded human-level performance in game playing AI with deep learning methods according to the experiments from DeepMind on Go and Atari games. Deep learning solves high dimension input problems which stop the development of reinforcement for many years. This study uses both two techniques to create several agents with different algorithms that successfully learn to play T-rex Runner. Deep Q network algorithm and three types of improvements are implemented to train the agent. The results from some of them are far from satisfactory but others are better than human experts. Batch normalization is a method to solve internal covariate shift problems in deep neural network. The positive influence of this on reinforcement learning has also been proved in this study. ii Acknowledgement I would like to express many thanks to my supervisor, Prof. -
When Are We Done with Games?
When Are We Done with Games? Niels Justesen Michael S. Debus Sebastian Risi IT University of Copenhagen IT University of Copenhagen IT University of Copenhagen Copenhagen, Denmark Copenhagen, Denmark Copenhagen, Denmark [email protected] [email protected] [email protected] Abstract—From an early point, games have been promoted designed to erase particular elements of unfairness within the as important challenges within the research field of Artificial game, the players, or their environments. Intelligence (AI). Recent developments in machine learning have We take a black-box approach that ignores some dimensions allowed a few AI systems to win against top professionals in even the most challenging video games, including Dota 2 and of fairness such as learning speed and prior knowledge, StarCraft. It thus may seem that AI has now achieved all of focusing only on perceptual and motoric fairness. Additionally, the long-standing goals that were set forth by the research we introduce the notions of game extrinsic factors, such as the community. In this paper, we introduce a black box approach competition format and rules, and game intrinsic factors, such that provides a pragmatic way of evaluating the fairness of AI vs. as different mechanical systems and configurations within one human competitions, by only considering motoric and perceptual fairness on the competitors’ side. Additionally, we introduce the game. We apply these terms to critically review the aforemen- notion of extrinsic and intrinsic factors of a game competition and tioned AI achievements and observe that game extrinsic factors apply these to discuss and compare the competitions in relation are rarely discussed in this context, and that game intrinsic to human vs.