"An Enjoyable Game" How HAL Plays Chess, Section 01
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												Pdf • Cynthia Breazeal
© copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org Human{Robot Interaction An Introduction Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, Selma Sabanovi´cˇ This material has been published by Cambridge University Press as Human Robot Interaction by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic. ISBN: 9781108735407 (http://www.cambridge.org/9781108735407). This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org This material has been published by Cambridge University Press as Human Robot Interaction by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic. ISBN: 9781108735407 (http://www.cambridge.org/9781108735407). This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org Contents List of illustrations viii List of tables xi 1 Introduction 1 1.1 About this book 1 1.2 Christoph - 
												
												Oral History of Hans Berliner
Oral History of Hans Berliner Interviewed by: Gardner Hendrie Recorded: March 7, 2005 Riviera Beach, Florida Total Running Time: 2:33:00 CHM Reference number: X3131.2005 © 2005 Computer History Museum CHM Ref: X3131.2005 © 2005 Computer History Museum Page 1 of 65 Q: Who has graciously agreed to do an oral history for the Computer History Museum. Thank you very much, Hans. Hans Berliner: Oh, you’re most welcome. Q: O.k. I think where I’d like to start is maybe a little further back than you might expect. I’d like to know if you could share with us a little bit about your family background. The environment that you grew up in. Your mother and father, what they did. Your brothers and sisters. Hans Berliner: O.k. Q: Where you were born. That sort of thing. Hans Berliner: O.k. I was born in Berlin in 1929, and we immigrated to the United States, very fortunately, in 1937, to Washington, D.C. As far as the family goes, my great uncle, who was my grandfather’s brother, was involved in telephone work at the turn of the previous century. And he actually owned the patent on the carbon receiver for the telephone. And they started a telephone company in Hanover, Germany, based upon his telephone experience. And he, later on, when Edison had patented the cylinder for recording, he’d had enough experience with sound recording that he said, “that’s pretty stupid”. And he decided to do the recording on a disc, and he successfully defended his patent in the Supreme Court, and so the patent on the phono disc belongs to Emile Berliner, who was my grand uncle. - 
												
												HC17.Computer History Museum Presentation
John Mashey Trustee www.computerhistory.org The Museum • Mission – To Preserve the Artifacts & Stories of the Information Age • Vision - Exploring the Computing Revolution and Its Impact on the Human Experience • Moving Forward: • Collecting over 25 years; started at Digital Equipment • Boston Artifacts moved to Silicon Valley in 1996 • Independent 501(c)(3) in July 1999 • New Home in Mountain View, CA in 2002 1401 N. Shoreline Dr, next to 101, with purple “on button” • June 2003: Phase 1 opened • Sept 2005: major new exhibit - Computer Chess www.computerhistory.org Our (Successive) Homes The Collection Behind the scenes: Largest collection of computing artifacts. Over 25 years of collecting! www.computerhistory.org The Collection Media Software Documentation Ephemera Hardware A World-Class Collection Lobby Exhibits: People & Innovation “Innovation 101”— Silicon Valley’s Contributions to Computer History www.computerhistory.org Exhibition Galleries: Now, Future Input/Output Now Processors Networking Visible Storage Software Storage Computing History Timeline Rotating Topical Exhibits www.computerhistory.org Virtual Visible Storage Activities • Speaker Series – History, history-in-the-making, special topics, exec briefs • Preservation Activities – Oral Histories - Active Collection of the Past – Videos & Photos - Proactive Collection for the Future • Restorations – Understand and restore environments of the past – IBM 1620; PDP-1; IBM 1401; – Many PC’s from study collection • Initial CyberMuseum – Virtual Visible Storage • Events – Seminars, - 
												
												Mastering Board Games
INSIGHTS | PERSPECTIVES COMPUTER SCIENCE community, with much analysis and com- mentary on the amazing style of play that Al- phaZero exhibited (see the figure). Note that Mastering board games neither the chess or shogi programs could take advantage of the TPU hardware that A single algorithm can learn to play three hard board games AlphaZero has been designed to use, making head-to-head comparisons more difficult. Chess, shogi, and Go are highly complex By Murray Campbell value in chess or shogi programs. The stron- but have a number of characteristics that gest programs in both games have relied on make them easier for AI systems. The game rom the earliest days of the computer variations of the alpha-beta algorithm, used state is fully observable; all the information era, games have been considered impor- in game-playing programs since the 1950s. needed to make a move decision is visible to tant vehicles for research in artificial in- Silver et al. demonstrated the power of the players. Games with partial observability, telligence (AI) (1). Game environments combining deep reinforcement learning such as poker, can be much more challenging, simplify many aspects of real-world with an MCTS algorithm to learn a variety although there have been notable successes problems yet retain sufficient complex- of games from scratch. The training method- in games like heads-up no-limit poker (11, 12). Fity to challenge humans and machines alike. ology used in AlphaZero is a slightly modi- Board games are also easy in other important Most programs for playing classic board fied version of that used in the predecessor dimensions. - 
												
												Computer Chess Methods
COMPUTER CHESS METHODS T.A. Marsland Computing Science Department, University of Alberta, EDMONTON, Canada T6G 2H1 ABSTRACT Article prepared for the ENCYCLOPEDIA OF ARTIFICIAL INTELLIGENCE, S. Shapiro (editor), D. Eckroth (Managing Editor) to be published by John Wiley, 1987. Final draft; DO NOT REPRODUCE OR CIRCULATE. This copy is for review only. Please do not cite or copy. Acknowledgements Don Beal of London, Jaap van den Herik of Amsterdam and Hermann Kaindl of Vienna provided helpful responses to my request for de®nitions of technical terms. They and Peter Frey of Evanston also read and offered constructive criticism of the ®rst draft, thus helping improve the basic organization of the work. In addition, my many friends and colleagues, too numerous to mention individually, offered perceptive observations. To all these people, and to Ken Thompson for typesetting the chess diagrams, I offer my sincere thanks. The experimental work to support results in this article was possible through Canadian Natural Sciences and Engineering Research Council Grant A7902. December 15, 1990 COMPUTER CHESS METHODS T.A. Marsland Computing Science Department, University of Alberta, EDMONTON, Canada T6G 2H1 1. HISTORICAL PERSPECTIVE Of the early chess-playing machines the best known was exhibited by Baron von Kempelen of Vienna in 1769. Like its relations it was a conjurer's box and a grand hoax [1, 2]. In contrast, about 1890 a Spanish engineer, Torres y Quevedo, designed a true mechanical player for king-and-rook against king endgames. A later version of that machine was displayed at the Paris Exhibition of 1914 and now resides in a museum at Madrid's Polytechnic University [2]. - 
												
												Sci Tech Laywer As Published
TRYING FOR THE TRIFECTA TELEHEALTH MEETS AI MEETS CYBERSECURITY By Thomas P. Keenan he social disruption of the COVID- and chest X-rays, after about five or six world—things like Wikipedia and so on— 19 pandemic has accelerated he’s getting tired, and after ten he’s “ready and used that data to learn how to answer Ttwo powerful trends in health to go the bar.” By contrast, the machine questions about the real world.”6 care—telemedicine (TM) and artificial treats each case with unbiased, fresh eyes, Today, we think nothing of saying, “Hey intelligence/machine learning (AI/ML). giving consistent results. “It [PUFF] also Google, turn off all the lights” or “Alexa, tell Advocates of digital transformation let out knows things I haven’t a clue about,” he me a joke about rabbits,” confident that our of collective “Yes!” as long-awaited changes added. One of the many experts who AI-enabled speakers will understand and happened almost overnight in early 2020. contributed knowledge to PUFF was a obey us. Because their knowledge base and Then, the ants of cybersecurity came along tropical medicine specialist. This contri- ML functionality reside in a remote com- to spoil the picnic! bution enabled the system to spot a rare puter, our virtual assistants will keep getting First, the good news. lung problem caused by breathing bat smarter and better informed. Perhaps • Suddenly, it became acceptable to guano in Central American caves. Alexa will have a joke about “one-legged “see” your doctor electronically; PUFF was an example of a rule-based rabbits” the next time I ask “her.”7 you could even send a picture of expert system. - 
												
												Deep Blue System Overview
Deep Blue System Overview Feng-hsiung Hsu, Murray S. Campbell, and A. Joseph Hoane, Jr. IBM T. J. Watson Research Center Abstract By the late 1970s, it became clear that faster hard- ware speed was the single most effective way to improve One of the oldest Grand Challenge problems in com- program performance, when used in conjunction with puter science is the creation of a World Championship brute force searching techniques. Joe Condon and Ken level chess computer. Combining VLSI custom circuits, Thompson [2] of Bell Laboratories proceeded to build dedicated massively parallel C@ search engines, and the famous Belle special-purpose chess automaton. In various new search algorithms, Deep Blue is designed 1982, searching about 8 plies (4 moves by White, and to be such a computer. This paper gives an overview 4 moves by Black) in chess middle games, it became of the system, and examines the prospects of reaching the first computer to play at USCF National Master the goal. level and thus claimed the first Fredkin Intermediate Prize. Thompson’s self-play experiments [13, 4] with Belle, however, showed that the performance increase 1 Introduction started to seriously taper off when the search depth exceeded 8 plies. At shallower depths, Belle gained Charles Babbage, back in 1840s, contemplated the pos- about 200 rating points (one full chess Class) for each sibility of playing chess using his Analytical Engine. additional ply, or close to 100 rating points for each Claude Shannon, at the dawn of the modern computer doubling of search speed. (To search one extra ply re- age, presented his seminal paper [10] on computer chess quires a six-fold increase in search speed on average. - 
												
												Game Playing in the Real World
GGaammee PPllaayyiinngg iinn tthhee RReeaall WWoorrlldd Next time: Knowledge Representation Reading: Chapter 7.1-7.3 1 WWhhaatt mmaatttteerrss?? ° Speed? ° Knowledge? ° Intelligence? ° (And what counts as intelligence?) ° Human vs. machine characteristics 2 ° The decisive game of the match was Game 2, which left a scare in my memory … we saw something that went well beyond our wildest expectations of how well a computer would be able to foresee the long-term positional consequences of its decisions. The machine refused to move to a position that had a decisive short-term advantage – showing a very human sense of danger. I think this moment could mark a revolution in computer science that could earn IBM and the Deep Blue team a Nobel Prize. Even today, weeks later, no other chess-playing program in the world has been able to evaluate correctly the consequences of Deep Blue’s position. (Kasparov, 1997) 3 QQuuootteess ffrroomm IIEEEEEE aarrttiiccllee ° Why, then, do the very best grandmasters still hold their own against the silicon beasts? ° The side with the extra half-move won 3 games out of four, corresponding to a 200-point gap in chess rating – roughly the difference between a typically grandmaster (about 2600) and Kasparov (2830) ° An opening innovation on move nine gave Kasparov not merely the superior game but one that Fritz could not understand ° Kasparov made sure that Fritz would never see the light at the end of that tunnel by making the tunnel longer. 4 DDeeeepp BBlluuee –– AA CCaassee SSttuuddyy Goals ° Win a match against - 
												
												The Regental Laureates Distinguished Presidential
REPORT TO CONTRIBUTORS Explore the highlights of this year’s report and learn more about how your generosity is making an impact on Washington and the world. CONTRIBUTOR LISTS (click to view) • The Regental Laureates • Henry Suzzallo Society • The Distinguished Presidential Laureates • The President’s Club • The Presidential Laureates • The President’s Club Young Leaders • The Laureates • The Benefactors THE REGENTAL LAUREATES INDIVIDUALS & ORGANIZATIONS / Lifetime giving totaling $100 million and above With their unparalleled philanthropic vision, our Regental Laureates propel the University of Washington forward — raising its profile, broadening its reach and advancing its mission around the world. Acknowledgement of the Regental Laureates can also be found on our donor wall in Suzzallo Library. Paul G. Allen & The Paul G. Allen Family Foundation Bill & Melinda Gates Bill & Melinda Gates Foundation Microsoft DISTINGUISHED PRESIDENTIAL LAUREATES INDIVIDUALS & ORGANIZATIONS / Lifetime giving totaling $50 million to $99,999,999 Through groundbreaking contributions, our Distinguished Presidential Laureates profoundly alter the landscape of the University of Washington and the people it serves. Distinguished Presidential Laureates are listed in alphabetical order. Donors who have asked to be anonymous are not included in the listing. Acknowledgement of the Distinguished Presidential Laureates can also be found on our donor wall in Suzzallo Library. American Heart Association The Ballmer Group Boeing The Foster Foundation Jack MacDonald* Robert Wood Johnson Foundation Washington Research Foundation * = Deceased Bold Type Indicates donor reached giving level in fiscal year 2016–2017 1 THE PRESIDENTIAL LAUREATES INDIVIDUALS & ORGANIZATIONS / Lifetime giving totaling $10 million to $49,999,999 By matching dreams with support, Presidential Laureates further enrich the University of Washington’s top-ranked programs and elevate emerging disciplines to new heights. - 
												
												Modern Masters of an Ancient Game
AI Magazine Volume 18 Number 4 (1997) (© AAAI) Modern Masters of an Ancient Game Carol McKenna Hamilton and Sara Hedberg he $100,000 Fredkin Prize for on Gary Kasparov in the final game of tute of Technology Computer Science Computer Chess, created in a tied, six-game match last May 11. Professor Edward Fredkin to encourage T1980 to honor the first program Kasparov had beaten the machine in continued research progress in com- to beat a reigning world chess champi- an earlier match held in February 1996. puter chess. The prize was three-tiered. on, was awarded to the inventors of The Fredkin Prize was awarded un- The first award of $5,000 was given to the Deep Blue chess machine Tuesday, der the auspices of AAAI; funds had two scientists from Bell Laboratories July 29, at the annual meeting of the been held in trust at Carnegie Mellon who in 1981 developed the first chess American Association for Artificial In- University. machine to achieve master status. telligence (AAAI) in Providence, The Fredkin Prize was originally es- Seven years later, the intermediate Rhode Island. tablished at Carnegie Mellon Universi- prize of $10,000 for the first chess ma- Deep Blue beat world chess champi- ty 17 years ago by Massachusetts Insti- chine to reach international master status was awarded in 1988 to five Carnegie Mellon graduate students who built Deep Thought, the precur- sor to Deep Blue, at the university. Soon thereafter, the team moved to IBM, where they have been ever since, working under wraps on Deep Blue. The $100,000 third tier of the prize was awarded at AAAI–97 to this IBM team, who built the first computer chess machine that beat a world chess champion. - 
												
												Chess As Played by Artificial Intelligence
Chess as Played by Artificial Intelligence Filemon Jankuloski, Adrijan Božinovski1 University American College of Skopje, Skopje, Macedonia [email protected] [email protected] Abstract. In this paper, we explore the evolution of how chess machinery plays chess. The purpose of the paper is to discuss the several different algorithms and methods which are implemented into chess machinery systems to either make chess playing more efficient or increase the playing strength of said chess machinery. Not only do we look at methods and algorithms implemented into chess machinery, but we also take a look at the many accomplishments achieved by these machines over a 70 year period. We will analyze chess machines from their conception up until their absolute domination of the competitive chess scene. This includes chess machines which utilize anything from brute force approaches to deep learning and neural networks. Finally, the paper is concluded by discussing the future of chess machinery and their involvement in both the casual and competitive chess scene. Keywords: Chess · Algorithm · Search · Artificial Intelligence 1 Introduction Artificial intelligence has become increasingly prevalent in our current society. We can find artificial intelligence being used in many aspects of our daily lives, such as through the use of Smartphones and autonomous driving vehicles such as the Tesla. There is no clear and concise definition for Artificial Intelligence, since it is a subject which still has massive space for improvement and has only recently been making most of its significant advancements. Leading textbooks on AI, however, define it as the study of “intelligent agents”, which can be represented by any device that perceives its environment and takes actions that maximizes its chances of achieving its goals [1]. - 
												
												CS415 Human Computer Interaction
CS415 Human Computer Interaction Lecture 11 – Advanced HCI Part-2 November 4, 2015 Sam Siewert Assignments Assignment #5 – Explore HCI’s, Propose Group Project (Groups of 3) Assignment #6 – Proof-of-Concept or Evaluation with Final Report Final Oral Exam – Presentation of Project Myself, Jim Weber, Dr. Davis, Dr. Bruder Emily and Adam Molson – Controls Lab Monitors – 9-5 on Sat, 12 noon – 9pm Sun Sam Siewert 2 Reality – Hybrid of 3 Models and … HCI – GOMS + Linguistic + Physical + Intelligence Newell and Simon [CMU, AI] Power of Recursion, 1958 NSS Chess – E.g. Minimax Search Herbert Alexander Simon, (June 15, 1916 – February 9, 2001) was an American scientist and artificial intelligence pioneer, economist and psychologist. Professor, most notably, at the Carnegie Mellon University, Pittsburgh, Pennsylvania, which became an important center of AI and computer chess, ...Herbert Simon received many top-level honors, most notably the Turing Award (with Allen Newell) (1975) for his AI- contributions [1] and the Nobel Memorial Prize in Economics for his pioneering research into the decision-making process within economic organizations (1978) [2 Allen Newell, (March 19, 1927 - July 19, 1992) was a American researcher in computer science and pioneer in the field of artificial intelligence and chess software [1] at the Carnegie Mellon University, Pittsburgh, Pennsylvania. In 1958, Allen Newell, Cliff Shaw, and Herbert Simon developed the chess program NSS [2]. It was written in a high-level language. Allen Newell and Herbert Simon were co-inventors of the alpha- beta algorithm, which was independently approximated or invented by John McCarthy, Arthur Samuel and Alexander Brudno [3].