Deep Blue

By Murray Campbell, A. Joseph Hoane Jr., Feng-Hsiung Hsu. 2002

1,460 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Sociology and Political Science, Reinforcement Learning

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Citations in the corpus

VAIN: Attentional Multi-agent Predictive Modeling (2017, neutral): “Approaches include [24, 25] for Chess, [26, 27, 28] for Backgammons [29] for Go.”
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, methodology): “Other strong chess programs, and also earlier programs such as Deep Blue, have used very similar architectures (9,24) including the majority of the components described above, although”
A Survey of Embodied AI: From Simulators to Research Tasks (2021, background): “Despite monumental technological advancements such as the AI systems that can beat most of the world champions in their respective games – such as chess [7], Go [8], and Atari games [3] – existing AI systems still do not possess the effectiveness and sophistication of a low-intelligence animal [9].”
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings (2021, background): “…component for achieving superhu-man performance in a number of hard benchmark problems in AI, including Go (Silver et al., 2016; 2017; 2018), Chess (Campbell et al., 2002), Poker (Moravˇc´ık et al., 2017; Brown & Sandholm, 2017; 2019), and, more recently, self-play Hanabi (Lerer et al., 2020).”
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization (2021, background): “…towards our ultimate goal of building intelligent agents like humans, from Shannon’s initial interest in Chess (Shannon, 1950) and IBM DeepBlue (Campbell et al., 2002), to the most recent deep reinforcement learning breakthroughs in Go (Silver et al., 2017), Dota II (OpenAI et al., 2019) and…”
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning (2019, methodology): “This includes examples such as Go (Silver et al., 2016; 2017; 2018), poker (Brown & Sandholm, 2017; Moravˇcík et al., 2017; Brown & Sandholm, 2019) and chess (Campbell et al., 2002).”
The Hanabi Challenge: A New Frontier for AI Research (2019, background): “Hanabi is different from the adversarial two-player zero-sum games where computers have reached super-human skill, e.g. , chess [9], checkers [10], go [11], backgammon [12] and two-player poker [13,14].”
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, methodology): “ and limiting if such knowledge is suboptimal. Yet many methods that have been applied to games have relied on human expert knowledge. Deep Blue used a human-engineered evaluation function for chess (Campbell et al., 2002). In computer Go, Maddison et al. (2015) and Clark and Storkey (2015) trained ”
Achieving Human Parity in Conversational Speech Recognition (2016, background): “Index Terms— Conversational speech recognition, convolutional neural networks, recurrent neural networks, VGG, ResNet, LACE, BLSTM, spatial smoothing.”
Deep Image: Scaling up Image Recognition (2015, background): “On May 11th, 1997, IBM’s Deep Blue achieved a historic victory by defeating world chess champion Gary Kasparov in a six-game match (Campbell et al., 2002).”
A Brief Survey of Deep Reinforcement Learning (2017, neutral): “The second standout success was the development of a hybrid DRL system, AlphaGo, that defeated a human world champion in Go [73], paralleling the historic achievement of IBM’s Deep Blue in chess two decades earlier [9].”
Places: An Image Database for Deep Scene Understanding (2016, background): “, and AlphaGo in Go against their expert human opponents may thus be seen as not just advances in algorithms, but the increasing availability of very large datasets: 700,000, 8.6 million, and 30 million items, respectively [6]– [8].”
"Other-Play" for Zero-Shot Coordination (2020, background): “If it converges, self-play ﬁnds a Nash equilibrium of the game and yields superhuman AI agents in two-player zero-sum games such as Chess, Go and Poker (Campbell et al., 2002; Silver et al., 2017; Brown & Sandholm, 2018).”
Machine behaviour (2019, background): “Excellent progress has been made on benchmark tasks, from board games like Chess 68 , Checkers 69 , and Go 70,71 to card games like Poker 72 , to computer games like those on the Atari platform 73 , to artificial markets 74 , to Robocup Soccer 75 , as well as standardized evaluation data such…”