Computer Chess Research Articles

Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies

TABLE OF CONTENTS I. INTRODUCTION II. THE TROUBLE WITH AI A. What Is AI? B. The Problematic Characteristics of AI 1. Autonomy, Foreseeability, and Causation 2. Control 3. Research and Development: Discreet, Diffuse, Discrete, and opaque C. A Role for the Law? III. INSTITUTIONAL COMPETENCE A. Legislatures 1. Democratic Legitimacy 2. Lack of Expertise 3. Delegation and oversight B. Agencies 1. Flexibility 2. Specialization and Expertise 3. Independence and Alienation 4. Ex Ante Action C. The Common Law Tort System 1. Fact-Finding 2. Reactive (and Reactionary) 3. Incrementalism 4. Misaligned Incentives IV. A REGULATORY PROPOSAL A. The Artificial Intelligence Development Act B. The Agency C. The Courts' Role V. CONCLUSION I. INTRODUCTION It may not always be obvious, but we are living in the age of intelligent machines. Artificial intelligence (AI) permeates our lives in numerous subtle and not-so-subtle ways, performing tasks that, until quite recently, could only be performed by a human with specialized knowledge, expensive training, or a government-issued license. Driverless cars have been approved for road operation in four states and the District of Columbia; (1) their inevitable arrival on the consumer market may revolutionize road transportation. Autonomous machines can execute complex financial transactions, flag potential terrorists using facial recognition software, and (most alarmingly for this author and his legal contemporaries) perform document review. (2) More mundanely, computer chess engines can defeat the strongest human players in the world, and Google Translate can generate passable English translations of Le Monde articles. In fact, robot journalists may even have written the Le Monde articles themselves. (3) The increasing ubiquity and rapidly expanding commercial potential of AI has spurred massive private sector investment in AI projects. Firms such as Google, Facebook, Amazon and Baidu have got into an AI arms race, poaching researchers, setting up laboratories and buying start-ups. (4) With each passing month, AI gains footholds in new industries and becomes more enmeshed in our day-to-day lives, and that trend seems likely to continue for the foreseeable future. (5) The potential for further rapid advances in AI technology has prompted expressions of alarm from many quarters, including some calls for government regulation of AI development and restrictions on AI operation. (6) That in and of itself is hardly surprising; fear of technological change and calls for the government to regulate new technologies are not new phenomena. What is striking about AI, however, is that leaders of the tech industry are voicing many of the concerns. Some of the concerns stem from the familiar fears of technological unemployment (7) and the potential for new technologies to be misused by humans. (8) But many of the fears cut much deeper. In an interview at MIT's 2014 AeroAstro Centennial Symposium, Elon Musk eschewed the skepticism of regulation that characterizes most of Silicon Valley's business titans and suggested that some government intervention might be wise in the case of artificial intelligence: I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it's probably that .... I'm increasingly inclined to think there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don't do something very foolish. (9) Other prominent figures in the tech world--most notably Bill Gates and Steve Wozniak--have voiced similar concerns regarding the long-term risks of AI. (10) At the very least, more mundane legal issues surrounding AI seem likely to crop up in the near future. Who (or what) will be held liable when an autonomous vehicle causes an accident? …

Computer Chess Endgame Play with Pawns: Then and Now

Computer Chess Endgame Play with Pawns: Then and Now

On the limits of engine analysis for cheating detection in chess

The integrity of online games has important economic consequences for both the gaming industry and players of all levels, from professionals to amateurs. Where there is a high likelihood of cheating, there is a loss of trust and players will be reluctant to participate — particularly if this is likely to cost them money.Chess is a game that has been established online for around 25 years and is played over the Internet commercially. In that environment, where players are not physically present “over the board” (OTB), chess is one of the most easily exploitable games by those who wish to cheat, because of the widespread availability of very strong chess-playing programs. Allegations of cheating even in OTB games have increased significantly in recent years, and even led to recent changes in the laws of the game that potentially impinge upon players’ privacy.In this work, we examine some of the difficulties inherent in identifying the covert use of chess-playing programs purely from an analysis of the moves of a game. Our approach is to deeply examine a large collection of games where there is confidence that cheating has not taken place, and analyse those that could be easily misclassified.We conclude that there is a serious risk of finding numerous “false positives” and that, in general, it is unsafe to use just the moves of a single game as prima facie evidence of cheating. We also demonstrate that it is impossible to compute definitive values of the figures currently employed to measure similarity to a chess-engine for a particular game, as values inevitably vary at different depths and, even under identical conditions, when multi-threading evaluation is used.

Genetic Algorithms for Evolving Computer Chess Programs

This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values of which are initialized randomly. The evaluation function of the program is evolved by learning from databases of (human) grandmaster games. At first, the organisms are evolved to mimic the behavior of human grandmasters, and then these organisms are further improved upon by means of coevolution. The search mechanism is evolved by learning from tactical test suites. Our results show that the evolved program outperforms a two-time world computer chess champion and is at par with the other leading computer chess programs.

Checkmate to deliberate practice: the case of Magnus Carlsen.

Checkmate to deliberate practice: the case of Magnus Carlsen.

About a Bot: Hoax, Fake, Performance Art

About a Bot: Hoax, Fake, Performance Art

What makes a chess program original? Revisiting the Rybka case

In this article I will consider the controversial Rybka case, which created a considerable stir in the computer chess community in 2011. Rybka was, from its initial public release in 2005 until it was surpassed in 2010, by far the strongest chess engine ever seen. The question of whether Rybka was substantially or slightly copied from other sources, or a completely original work, is still passionately being debated on forums and blogs. Since the actual source code was not available the determination of whether or not the program was original is complex and involves technical topics such as reverse engineering, program analysis, the abstraction–filtration–comparison test and black box testing such as ponder-hit analysis. The question relates to practices for software development, legal issues like copyright law and touches on innovation and market leadership.Drawing on this array of topics we review the question if and to what extent Rybka was a clone/derivative.The main conclusion is that the view computer experts take on this question is colored by two different views of software development, one that fits programming in an academic environment, one that fits programming in an industry environment.This article is party based on my four part ChessBase article, but includes some new analysis.

Analysis of Performance of Consultation Methods in Computer Chess

The performance of consultation methods, i.e., majority voting, the optimistic selection rule, and the pseudo-random number (PRN) ensemble method, are examined in computer chess using 2180 chess problems. Here, the optimistic selection rule selects a program that returns the highest search value, and the PRN ensemble consists of multiple individual copies of one base program, and each copy is diversified by adding random numbers to the evaluation function of the base program. We carried out empirical experiments by using state-of-the-art chess-program Crafty as the base program. We found that the percentage of correct answers increased from 55.6 to 57.1% using optimistic selection from the PRN ensemble. The experimental results indicated that the consultation methods allowed simple yet effective distributed computing in chess.

Plagiarism in game programming competitions

On June 28, 2011 the International Computer Games Association (ICGA) disqualified and banned the program Rybka and its programmer Vasik Rajlich from previous and future World Computer Chess Championships (WCCC). The ICGA had conducted an investigation into allegations that, in the chess program Rybka, two other programs were plagiarized: Crafty and Fruit. It was found that the allegations were true, and that the ICGA tournament rules had been broken. The investigation, the report of the investigation, and the verdict that Rajlich was guilty of the plagiarism took place in the form of a version of Crowdsourced Online Dispute Resolution (CODR). The above sentence was determined by the Board of the ICGA. This article describes, amongst other things, the background, the ICGA rules, the rules for fair play in competitions, CODR, and the future of clones. Finally, in the conclusions, the question is addressed whether the application of the ICGA rules has been fair and lawful.

The 20th World Computer Chess Championship

The 20th World Computer Chess Championship

The 3rd World Chess Software Championship

The 3rd World Chess Software Championship

Trap<sup>A</sup><sub>B</sub>: A New Position Search Algorithm in Chinese Chess Computer Game

In this paper, we propose an improved alpha-beta search algorithm, named trappy alpha-beta (simply), for game-tree in order to identify and set the potential traps in the game playing. can be regarded as an extension of the traditional alpha-beta search algorithm which ties to predict when the opponent might make a mistake and select such moves that can most likely lead the an opponent into the trap by comparing the various scores returned through iterative deepening technology. In our experiment, we test the performance of in comparison with three game-tree search algorithms, i.e., min-max, trappy minimax, and alpha-beta, by playing with four testing opponents, which are obtained form a typical Chinese chess computer game program-Xqwizard (http://www.xqbase.com). The comparative results show that our designedcan effectively find and set the traps in the playing with opponents.

Ole Bjerg, Poker: The Parody of Capitalism

Ole Bjerg, Poker: Parody of Capitalism, Ann Arbour, MI: University of Michigan Press, 2011, 274 pp. $35.00 (ISBN 978-0-472-05163-2, pbk.) begins with the issue of the contemporary popularity of poker and makes the case that the game should be treated much like other phenomena, such as art and literature. While the discourse around poker manifested in law, morality, and politics--is it a game of chance or skill?--has contributed to ambivalence, poker deserves to be treated as richly culturally expressive. Following on Roger Caillois' insights into the relationship between games, cultures, and social structures, Bjerg's analysis of poker contributes to the sociology of games. Further, and as the title makes clear, poker is the parody of capitalism. book provides a sophisticated, philosophically informed, and enjoyable analysis of poker, its cultural resonance, and parodic relationship to contemporary, postindustrial capitalism. It draws on a variety of philosophical positions to illuminate the game: from probability theory and game theory to early and late Wittgensteinian arguments about language. Most important, and central to Bjerg's approach, is the work of analyst Slavoj Zizek and the deployment of the Lacanian-derived concepts of the real, symbolic, and imaginary. These concepts are laid out in the first chapter of the book, The Ontology of Poker, where they are applied comparatively to chess, roulette, and rock-paper-scissors. Where chess, for example, is a game that grounds itself in the symbolic order --since there are always optimal moves that can be mapped out (think chess computer Deep Blue), poker is unique among games in that it invokes all three ontological orders, and where awareness of the imaginary dimension separates the good players from weaker ones. book is divided into four parts with two or three chapters in each: Part One explores The Philosophy of Poker. Part Two focuses on The Economy. Part Three develops the typology, Suckers, Grinders, and and Part Four discusses Poker and Capitalism. As the book includes some descriptive analysis of the movement of money in the online poker economy in Part Two, and for the sake of length, the review will focus on Parts One, Three, and Four. In Part One, Anatomy of a Hand, and All you ever wanted to know about Texas Hold 'Em, Bjerg analyzes strategy and illuminates the game with the philosophical sources mentioned. players will appreciate the analysis of a hand by poker professional Gus Hansen in Anatomy ... , while in the latter chapter, Bjerg provides a critique of the application of game theory to poker: it is too caught up in the symbolic to grasp the game's imaginary dimension. In these chapters, Bjerg demonstrates a deep appreciation for, and theoretically sophisticated analysis of the game. parodic relationship with capitalism is developed in the later chapters. In Part Three (after analyzing playing styles in the final chapter of Part Two), Bjerg develops his tripartite typology of poker players/strategies. Each type corresponds with one of the ontological orders. Suckers are luck players who desire action and play loosely. Their orientation to chance and to action sees them as representatives of the real: they orient to the lucky chance of hitting their card--the unpredictable manifestation of the real, but this approach is a losing strategy. Grinders are the mathematically oriented calculators, tight players who do not veer from the optimal mathematical strategy and are guided by long run probabilities. Their probabilistic orientation places them in the realm of the symbolic. final category, Players, are the adepts of the poker imaginary, knowing how to bluff and dissemble and thus superior to the other types. While they too know the mathematical odds and how to value their own and their opponents' hands, they know the importance of reading the other players. …

Turing, Kasparov and the Future

The 'Turing 100' Conference in Manchester was the main event of the Turing Centenary Year in 2012. This is a report and reflection on Kasparov's popular talk. Within it, he explained how Turing and influenced computer chess, his career and the chess community. Kasparov also played Chessbase's 'TURING' emulation of Turing's second paper chess engine, here labelled 'AT2'. Quasi Turing-tests, computer contributions to world championship chess, and suspected cheating in chess are also mentioned.

Mental disorders as “brain diseases” and Jaspers’ legacy

Mental disorders as “brain diseases” and Jaspers’ legacy

Tree Pruning for New Search Techniques in Computer Games

This paper proposes a new mechanism for pruning a search game tree in computer chess. The algorithm stores and then reuses chains or sequences of moves, built up from previous searches. These move sequences have a built-in forward-pruning mechanism that can radically reduce the search space. A typical search process might retrieve a move from a Transposition Table, where the decision of what move to retrieve would be based on the position itself. This algorithm stores move sequences based on what previous sequences were better, or caused cutoffs. The sequence is then returned based on the current move only. This is therefore position independent and could also be useful in games with imperfect information or uncertainty, where the whole situation is not known at any one time. Over a small set of tests, the algorithm was shown to clearly out perform Transposition Tables, both in terms of search reduction and game-play results. Finally, a completely new search process will be suggested for computer chess or games in general.

Computing Deep Perft and Divide Numbers for Checkers

The perft method, originating from the chess programming community, has become a widespread way to test the correctness and performance of move generators. Although its usefulness diminishes with depth, computing deep perft numbers poses an interesti

The 2nd World Chess Software Championship

The 2nd World Chess Software Championship

Knowledge Discovery in Chess Using an Aesthetics Approach

Computational aesthetics is a relatively new subfield of artificial intelligence (AI). It includes research that enables computers to “recognize” (and evaluate) beauty in various domains such as visual art, music, and games. Aside from the benefit this gives to humans in terms of creating and appreciating art in these domains, there are perhaps also philosophical implications about the nature and “mechanics” of aesthetic perception in humans. We can, potentially, learn more about ourselves as we replicate or simulate this ability in machines. My original intention or objective was to develop an aesthetics model or method for computers to recognize beauty in the game of chess since it is greatly appreciated by human players1—there is even an entire subdomain devoted to it in the form of chess compositions2—and because it has other promising applications, such as improving computer chess heuristics, for example, in solving complex chess problems. My intention was also that this model should correlate positively and reasonably well with human player aesthetic assessment. Both were achieved with better experimentally validated success than previous approaches.3 A computer program called CHESTHETICA (see appendix) was developed to aid in the complex calculations involved. It can identify beautiful combinations4 in large databases of chess games based on the aesthetics model.5

Intrinsic Chess Ratings

This paper develops and tests formulas for representing playing strength at chess by the quality of moves played, rather than by the results of games. Intrinsic quality is estimated via evaluations given by computer chess programs run to high depth, ideally so that their playing strength is sufficiently far ahead of the best human players as to be a `relatively omniscient' guide. Several formulas, each having intrinsic skill parameters s for `sensitivity' and c for `consistency', are argued theoretically and tested by regression on large sets of tournament games played by humans of varying strength as measured by the internationally standard Elo rating system. This establishes a correspondence between Elo rating and the parameters. A smooth correspondence is shown between statistical results and the century points on the Elo scale, and ratings are shown to have stayed quite constant over time. That is, there has been little or no `rating inflation'. The theory and empirical results are transferable to other rational-choice settings in which the alternatives have well-defined utilities, but in which complexity and bounded information constrain the perception of the utility values.