The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

Deep Blue is the chess machine that defeated then-reigning World Chess Champion Garry Kasparov in a six-game match in 1997. There were a number of factors that contributed to this success, including: a single-chip chess search engine, a massively parallel system with multiple levels of parallelism, a strong emphasis on search extensions, a complex evaluation function, and effective use of a Grandmaster game database. This paper describes the Deep Blue system, and gives some of the rationale that went into the design decision behind Deep Blue. Copyright 2001 Elsevier B.V.

Deep Blue

A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

In this paper we present a broad overview of the last 40 years of research on cognitive architectures To date, the number of existing architectures has reached several hundred, but most of the existing surveys do not reflect this growth and instead focus on a handful of well-established architectures In this survey we aim to provide a more inclusive and high-level overview of the research on cognitive architectures Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning, reasoning and metareasoning In order to assess the breadth of practical applications of cognitive architectures we present information on over 900 practical projects implemented using the cognitive architectures in our list We use various visualization techniques to highlight the overall trends in the development of the field In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress

/pdf/40-years-of-cognitive-architectures-core-cognitive-abilities-1du0zlp1df.pdf

40 years of cognitive architectures: core cognitive abilities and practical applications

https://www.cs.utexas.edu/~pstone/Courses/394Rspring13/resources/mcrave.pdf

Monte-Carlo tree search and rapid action value estimation in computer Go

How Computers Play Chess

Machines that learn to play games

Memory versus Search in Games

Games in ai research

How to Extract Relevant Knowledge from Grandmaster Games: PART 2: The Notion of Mobility, and the Work of De Groot and Slater

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