Expertise in chess

This chapter provides an overview of research into chess expertise. After an historical background and a brief description of the game and the rating system, it discusses the information processes enabling players to choose good moves, and in particular the trade-offs between knowledge and search. Other topics include blindfold chess, talent, and the role of deliberate practice and tournament experience

Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging the hidden knowledge encoded within these highly performant AI systems. Yet, this knowledge is often hard to extract, and may be hard to understand or learn from. Here, we show that this is possible by proposing a new method that allows us to extract new chess concepts in AlphaZero, an AI system that mastered the game of chess via self-play without human supervision. Our analysis indicates that AlphaZero may encode knowledge that extends beyond the existing human knowledge, but knowledge that is ultimately not beyond human grasp, and can be successfully learned from. In a human study, we show that these concepts are learnable by top human experts, as four top chess grandmasters show improvements in solving the presented concept prototype positions. This marks an important first milestone in advancing the frontier of human knowledge by leveraging AI; a development that could bear profound implications and help us shape how we interact with AI systems across many AI applications.Comment: 61 pages, 29 figure

Specialization effect and its influence on memory and problem solving in expert chess players

Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise – players remembering positions and solving problems from their area of specialization performed at around the level of players one standard deviation above them in general skill. Their problem solving strategy also changed depending on whether the problem was within their area of specialization or not. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context independent general purpose problem solving strategies to teach to future experts is unlikely to be successful

Competing fantasies of humans and machines: Symbolic convergences in artificial intelligence events coverage

This research analyzes coverage of major artificial intelligence events representing the thematic concept of "man versus machine." Rooted in grounded theory and rhetorical criticism, this research applies symbolic convergence theory and fantasy theme analysis to reporting from The New York Times, The Wall Street Journal and The Washington Post immediately surrounding three cultural and scientific milestones in the development of artificial intelligence technology: IBM Deep Blue's 1997 defeat of chess grandmaster Garry Kasparov; IBM Watson's 2011 defeat of Jeopardy! champions Ken Jennings and Brad Rutter; and Google DeepMind AlphaGo's 2016 defeat of Lee Sedol. This research analyzes how symbolic realities are dramatized in the context of these events such that the competitions themselves represent ideological battles between humanism or technological superiority. This research also demonstrates subtle variations in how fantasy themes and rhetorical visions manifest in coverage from each outlet, amounting to what is effectively a competition for shared consciousness between these two competing ideological constructs

Collaborative computer personalities in the game of chess

Computer chess has played a crucial role in Artificial Intelligence research since the creation of the modem computer. It has gained this prominent position due to the large domain that it encompasses, including psychology, philosophy and computer science. The new and innovative techniques initially created for computer chess have often been successfully transferred to other divergent research areas such as theorem provers and economic models. The progress achieved by computers in the game of chess has been illustrated by Deep Blue’s famous victory over Garry Kasparov in 1997. However, further improvements are required if more complex problems are to be solved. In 1999 the Kasparov versus the World match took place over the Internet. The match allowed chess players from around the world to collaborate in a single game of chess against the then world champion, Garry Kasparov. The game was closely fought with Kasparov coming out on top. One of the most surprising aspects of the contest was the high quality of play achieved by the World team. The World team consisted of players with varying skill and style of play, despite this they achieved a level of play that was considered better than any of its individual members. The purpose of this research is to investigate if collaboration by different players can be successfully transferred to the domain of computer chess

Chess software and its impact on chess players

Computer-aided chess is an important teaching method, as it allows a student to play under every condition possible, and regulates the speed of his/her development at an incremental pace, measured against actual players in the rated chess community. It is also relatively inexpensive, and pervasive, and allows players to match themselves against competitors from across the world. The learning process extends beyond games, as interactive software has shown it teaches several skills, such as opening, strategy, tactics, and chess-problem solving. Furthermore, current applications allow chess players to establish rankings via online chess tournaments, meet international grandmasters, and have access to training tools based on strategies from chess masters. Using 250 chess software packages, this research classifies them into distinct categories based mainly on the Gobet and Jansen's organization of the chess knowledge. This is followed by extensive discussion that analyzes these training tools, in order to identify the best training techniques available building on a research on human computer interaction, cognitive psychology, and chess theory. --P.ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b151379

Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain

Acquisition of Chess Knowledge in AlphaZero

What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts, our ability to understand faithful explanations of their decisions will be restricted, ultimately limiting what we can achieve with neural network interpretability. In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess. By probing for a broad range of human chess concepts we show when and where these concepts are represented in the AlphaZero network. We also provide a behavioural analysis focusing on opening play, including qualitative analysis from chess Grandmaster Vladimir Kramnik. Finally, we carry out a preliminary investigation looking at the low-level details of AlphaZero's representations, and make the resulting behavioural and representational analyses available online.Comment: 69 pages, 44 figure