Depth, Balancing, and Limits of the Elo Model
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ENDER's GAME by Orson Scott Card Chapter 1 -- Third
ENDER'S GAME by Orson Scott Card Chapter 1 -- Third "I've watched through his eyes, I've listened through his ears, and tell you he's the one. Or at least as close as we're going to get." "That's what you said about the brother." "The brother tested out impossible. For other reasons. Nothing to do with his ability." "Same with the sister. And there are doubts about him. He's too malleable. Too willing to submerge himself in someone else's will." "Not if the other person is his enemy." "So what do we do? Surround him with enemies all the time?" "If we have to." "I thought you said you liked this kid." "If the buggers get him, they'll make me look like his favorite uncle." "All right. We're saving the world, after all. Take him." *** The monitor lady smiled very nicely and tousled his hair and said, "Andrew, I suppose by now you're just absolutely sick of having that horrid monitor. Well, I have good news for you. That monitor is going to come out today. We're going to just take it right out, and it won't hurt a bit." Ender nodded. It was a lie, of course, that it wouldn't hurt a bit. But since adults always said it when it was going to hurt, he could count on that statement as an accurate prediction of the future. Sometimes lies were more dependable than the truth. "So if you'll just come over here, Andrew, just sit right up here on the examining table. -
CSC321 Lecture 23: Go
CSC321 Lecture 23: Go Roger Grosse Roger Grosse CSC321 Lecture 23: Go 1 / 22 Final Exam Monday, April 24, 7-10pm A-O: NR 25 P-Z: ZZ VLAD Covers all lectures, tutorials, homeworks, and programming assignments 1/3 from the first half, 2/3 from the second half If there's a question on this lecture, it will be easy Emphasis on concepts covered in multiple of the above Similar in format and difficulty to the midterm, but about 3x longer Practice exams will be posted Roger Grosse CSC321 Lecture 23: Go 2 / 22 Overview Most of the problem domains we've discussed so far were natural application areas for deep learning (e.g. vision, language) We know they can be done on a neural architecture (i.e. the human brain) The predictions are inherently ambiguous, so we need to find statistical structure Board games are a classic AI domain which relied heavily on sophisticated search techniques with a little bit of machine learning Full observations, deterministic environment | why would we need uncertainty? This lecture is about AlphaGo, DeepMind's Go playing system which took the world by storm in 2016 by defeating the human Go champion Lee Sedol Roger Grosse CSC321 Lecture 23: Go 3 / 22 Overview Some milestones in computer game playing: 1949 | Claude Shannon proposes the idea of game tree search, explaining how games could be solved algorithmically in principle 1951 | Alan Turing writes a chess program that he executes by hand 1956 | Arthur Samuel writes a program that plays checkers better than he does 1968 | An algorithm defeats human novices at Go 1992 -
Residual Networks for Computer Go Tristan Cazenave
Residual Networks for Computer Go Tristan Cazenave To cite this version: Tristan Cazenave. Residual Networks for Computer Go. IEEE Transactions on Games, Institute of Electrical and Electronics Engineers, 2018, 10 (1), 10.1109/TCIAIG.2017.2681042. hal-02098330 HAL Id: hal-02098330 https://hal.archives-ouvertes.fr/hal-02098330 Submitted on 12 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. IEEE TCIAIG 1 Residual Networks for Computer Go Tristan Cazenave Universite´ Paris-Dauphine, PSL Research University, CNRS, LAMSADE, 75016 PARIS, FRANCE Deep Learning for the game of Go recently had a tremendous success with the victory of AlphaGo against Lee Sedol in March 2016. We propose to use residual networks so as to improve the training of a policy network for computer Go. Training is faster than with usual convolutional networks and residual networks achieve high accuracy on our test set and a 4 dan level. Index Terms—Deep Learning, Computer Go, Residual Networks. I. INTRODUCTION Input EEP Learning for the game of Go with convolutional D neural networks has been addressed by Clark and Storkey [1]. It has been further improved by using larger networks [2]. -
Achieving Master Level Play in 9X9 Computer Go
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 9 Computer Go × Sylvain Gelly∗ David Silver† Univ. Paris Sud, LRI, CNRS, INRIA, France University of Alberta, Edmonton, Alberta, Canada Abstract simulated, using self-play, starting from the current position. Each position in the search tree is evaluated by the average The UCT algorithm uses Monte-Carlo simulation to estimate outcome of all simulated games that pass through that po- the value of states in a search tree from the current state. However, the first time a state is encountered, UCT has no sition. The search tree is used to guide simulations along knowledge, and is unable to generalise from previous expe- promising paths. This results in a highly selective search rience. We describe two extensions that address these weak- that is grounded in simulated experience, rather than an ex- nesses. Our first algorithm, heuristic UCT, incorporates prior ternal heuristic. Programs using UCT search have outper- knowledge in the form of a value function. The value function formed all previous Computer Go programs (Coulom 2006; can be learned offline, using a linear combination of a million Gelly et al. 2006). binary features, with weights trained by temporal-difference Monte-Carlo tree search algorithms suffer from two learning. Our second algorithm, UCT–RAVE, forms a rapid sources of inefficiency. First, when a position is encoun- online generalisation based on the value of moves. We ap- tered for the first time, no knowledge is available to guide plied our algorithms to the domain of 9 9 Computer Go, the search. -
Computer Go: from the Beginnings to Alphago Martin Müller, University of Alberta
Computer Go: from the Beginnings to AlphaGo Martin Müller, University of Alberta 2017 Outline of the Talk ✤ Game of Go ✤ Short history - Computer Go from the beginnings to AlphaGo ✤ The science behind AlphaGo ✤ The legacy of AlphaGo The Game of Go Go ✤ Classic two-player board game ✤ Invented in China thousands of years ago ✤ Simple rules, complex strategy ✤ Played by millions ✤ Hundreds of top experts - professional players ✤ Until 2016, computers weaker than humans Go Rules ✤ Start with empty board ✤ Place stone of your own color ✤ Goal: surround empty points or opponent - capture ✤ Win: control more than half the board Final score, 9x9 board ✤ Komi: first player advantage Measuring Go Strength ✤ People in Europe and America use the traditional Japanese ranking system ✤ Kyu (student) and Dan (master) levels ✤ Separate Dan ranks for professional players ✤ Kyu grades go down from 30 (absolute beginner) to 1 (best) ✤ Dan grades go up from 1 (weakest) to about 6 ✤ There is also a numerical (Elo) system, e.g. 2500 = 5 Dan Short History of Computer Go Computer Go History - Beginnings ✤ 1960’s: initial ideas, designs on paper ✤ 1970’s: first serious program - Reitman & Wilcox ✤ Interviews with strong human players ✤ Try to build a model of human decision-making ✤ Level: “advanced beginner”, 15-20 kyu ✤ One game costs thousands of dollars in computer time 1980-89 The Arrival of PC ✤ From 1980: PC (personal computers) arrive ✤ Many people get cheap access to computers ✤ Many start writing Go programs ✤ First competitions, Computer Olympiad, Ing Cup ✤ Level 10-15 kyu 1990-2005: Slow Progress ✤ Slow progress, commercial successes ✤ 1990 Ing Cup in Beijing ✤ 1993 Ing Cup in Chengdu ✤ Top programs Handtalk (Prof. -
Reinforcement Learning of Local Shape in the Game of Go
Reinforcement Learning of Local Shape in the Game of Go David Silver, Richard Sutton, and Martin Muller¨ Department of Computing Science University of Alberta Edmonton, Canada T6G 2E8 {silver, sutton, mmueller}@cs.ualberta.ca Abstract effective. They are fast to compute; easy to interpret, modify and debug; and they have good convergence properties. We explore an application to the game of Go of Secondly, weights are trained by temporal difference learn- a reinforcement learning approach based on a lin- ing and self-play. The world champion Checkers program ear evaluation function and large numbers of bi- Chinook was hand-tuned by expert players over 5 years. nary features. This strategy has proved effective When weights were trained instead by self-play using a tem- in game playing programs and other reinforcement poral difference learning algorithm, the program equalled learning applications. We apply this strategy to Go the performance of the original version [7]. A similar ap- by creating over a million features based on tem- proach attained master level play in Chess [1]. TD-Gammon plates for small fragments of the board, and then achieved world class Backgammon performance after train- use temporal difference learning and self-play. This ingbyTD(0)andself-play[13]. A program trained by method identifies hundreds of low level shapes with TD(λ) and self-play outperformed an expert, hand-tuned ver- recognisable significance to expert Go players, and sion at the card game Hearts [11]. Experience generated provides quantitive estimates of their values. We by self-play was also used to train the weights of the world analyse the relative contributions to performance of champion Othello and Scrabble programs, using least squares templates of different types and sizes. -
Strategic Choices: Small Budgets and Simple Regret Cheng-Wei Chou, Ping-Chiang Chou, Chang-Shing Lee, David L
Strategic Choices: Small Budgets and Simple Regret Cheng-Wei Chou, Ping-Chiang Chou, Chang-Shing Lee, David L. Saint-Pierre, Olivier Teytaud, Mei-Hui Wang, Li-Wen Wu, Shi-Jim Yen To cite this version: Cheng-Wei Chou, Ping-Chiang Chou, Chang-Shing Lee, David L. Saint-Pierre, Olivier Teytaud, et al.. Strategic Choices: Small Budgets and Simple Regret. TAAI, 2012, Hualien, Taiwan. hal-00753145v2 HAL Id: hal-00753145 https://hal.inria.fr/hal-00753145v2 Submitted on 18 Mar 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Strategic Choices: Small Budgets and Simple Regret Cheng-Wei Chou 1, Ping-Chiang Chou, Chang-Shing Lee 2, David Lupien Saint-Pierre 3, Olivier Teytaud 4, Mei-Hui Wang 2, Li-Wen Wu 2 and Shi-Jim Yen 2 1Dept. of Computer Science and Information Engineering NDHU, Hualian, Taiwan 2Dept. of Computer Science and Information Engineering National University of Tainan, Taiwan 3Montefiore Institute Universit´ede Li`ege, Belgium 4TAO (Inria), LRI, UMR 8623(CNRS - Univ. Paris-Sud) bat 490 Univ. Paris-Sud 91405 Orsay, France March 18, 2013 Abstract In many decision problems, there are two levels of choice: The first one is strategic and the second is tactical. -
Weiqi, Baduk): a Beautiful Game
Go (Weiqi, Baduk): A Beautiful Game Description Go is a strategy board game originating from China more than two thousand years ago. Though its rules are simple, the game is intractably complex; a top professional can easily defeat the best supercomputers today. It was considered one of the four essential skills of a Chinese scholar. The game spread to Korea and Japan, has flourished in East Asia, and is becoming more well-known in the West. Today, Go is enjoyed by more than 40 million players in the world. Objectives Students will learn the rules of go and become able to play at an intermediate level. They will also understand the game in a historical and/or mathematical context. We hope (but do not require) that students will be able to play at a single-digit kyu level (1k-9k). Students will also learn something significant from one of the following (or both): ◦ The culture and history of go, including its origins, historical figure, and presence in East Asia and the western world. ◦ Some mathematical results of go, including a treatment of go using combinatorial game theory. This will offer enough flexibility for students of all majors. Prerequisites None. We expect all students to be able to understand the course material. The course is designed to suit students of all majors. This course is not designed for dan-level players. We will not allow dan-level players to take this course. Course Format This course will be taught by Joshua Wu in both lecture and interactive formats. We hope to emphasize student participation. -
Learning to Play the Game of Go
Learning to Play the Game of Go James Foulds October 17, 2006 Abstract The problem of creating a successful artificial intelligence game playing program for the game of Go represents an important milestone in the history of computer science, and provides an interesting domain for the development of both new and existing problem-solving methods. In particular, the problem of Go can be used as a benchmark for machine learning techniques. Most commercial Go playing programs use rule-based expert systems, re- lying heavily on manually entered domain knowledge. Due to the complexity of strategy possible in the game, these programs can only play at an amateur level of skill. A more recent approach is to apply machine learning to the prob- lem. Machine learning-based Go playing systems are currently weaker than the rule-based programs, but this is still an active area of research. This project compares the performance of an extensive set of supervised machine learning algorithms in the context of learning from a set of features generated from the common fate graph – a graph representation of a Go playing board. The method is applied to a collection of life-and-death problems and to 9 × 9 games, using a variety of learning algorithms. A comparative study is performed to determine the effectiveness of each learning algorithm in this context. Contents 1 Introduction 4 2 Background 4 2.1 Go................................... 4 2.1.1 DescriptionoftheGame. 5 2.1.2 TheHistoryofGo ...................... 6 2.1.3 Elementary Strategy . 7 2.1.4 Player Rankings and Handicaps . 7 2.1.5 Tsumego .......................... -
Human Vs. Computer Go: Review and Prospect
This article is accepted and will be published in IEEE Computational Intelligence Magazine in August 2016 Human vs. Computer Go: Review and Prospect Chang-Shing Lee*, Mei-Hui Wang Department of Computer Science and Information Engineering, National University of Tainan, TAIWAN Shi-Jim Yen Department of Computer Science and Information Engineering, National Dong Hwa University, TAIWAN Ting-Han Wei, I-Chen Wu Department of Computer Science, National Chiao Tung University, TAIWAN Ping-Chiang Chou, Chun-Hsun Chou Taiwan Go Association, TAIWAN Ming-Wan Wang Nihon Ki-in Go Institute, JAPAN Tai-Hsiung Yang Haifong Weiqi Academy, TAIWAN Abstract The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications. I. Computer Go Competitions The IEEE Computational Intelligence Society (CIS) has funded human vs. computer Go competitions in IEEE CIS flagship conferences since 2009. -
The Design and Development of a 9 by 9 GO Opening Game Knowledge Base System
International Journal of Information and Education Technology, Vol. 3, No. 4, August 2013 The Design and Development of a 9 by 9 GO Opening Game Knowledge Base System Chun-Hsiang Hsieh and Jeng-Chi Yan computer GO games [2], [3]. Abstract—This study used the hashing method to produce a 9 by 9 GO opening game knowledge base system, and conducted B. Concept of Knowledge Base System in Opening Game in-depth study to solve the various board situation symmetry In the game of 9 by 9 GO, as the board territory is problems. This system can analyze the winning rate of various relatively smaller, if the opening game is not well-played, the moves in the opening game. If a sufficient number of chess player may easily land in a disadvantageous situation at manuals can be inputted into the system, it can effectively beginning. However, in this case, if the global search system provide move choices in the computer GO opening game, and can be used for the references of GO players on opening games. is used, the results are not necessarily ideal; one of the In sum, according to this knowledge base system of this article, reasons is that there are too many points for selection, and it this study considers that computer GO may defeat human is difficult to distinguish good choices. As a result, the search experts perfectly in the future. time will increase considerably, while the results are not necessarily correct. For example, as shown in Fig. 1, after the Index Terms—Hashing method, knowledge base system in move of black 1, it is white’s turn, even if the symmetric parts opening game, computer GO. -
Active Opening Book Application for Monte-Carlo Tree Search in 19×19 Go
Active Opening Book Application for Monte-Carlo Tree Search in 19×19 Go Hendrik Baier and Mark H. M. Winands Games and AI Group, Department of Knowledge Engineering Maastricht University, Maastricht, The Netherlands Abstract The dominant approach for programs playing the Asian board game of Go is nowadays Monte-Carlo Tree Search (MCTS). However, MCTS does not perform well in the opening phase of the game, as the branching factor is high and consequences of moves can be far delayed. Human knowledge about Go openings is typically captured in joseki, local sequences of moves that are considered optimal for both players. The choice of the correct joseki in a given whole-board position, however, is difficult to formalize. This paper presents an approach to successfully apply global as well as local opening moves, extracted from databases of high-level game records, in the MCTS framework. Instead of blindly playing moves that match local joseki patterns (passive opening book application), knowledge about these moves is integrated into the search algorithm by the techniques of move pruning and move biasing (active opening book application). Thus, the opening book serves to nudge the search into the direction of tried and tested local moves, while the search is able to filter out locally optimal, but globally problematic move choices. In our experiments, active book application outperforms passive book application and plain MCTS in 19×19 Go. 1 Introduction Since the early days of Artificial Intelligence (AI) as a scientific field, games have been used as testbed and benchmark for AI architectures and algorithms. The board game of Go, for example, has stimulated con- siderable research efforts over the last decades, second only to computer chess.