Man Versus Machine: Kasparov Versus Deep Blue

Goodman, David; Keene, Raymond

doi:10.3233/icg-1997-20308

Cited by 15 publications

(7 citation statements)

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“…Examples of computers artificial intelligence (AI) programs outperforming humans can be traced back to 1997, when the IBM’s Deep Blue first overcame the world chess champion Kasparov (Goodman & Keene, 1977). Recently in 2015, AlphaGo from Google’s DeepMind lab beats the top players in Go (Silver et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Dr. Eureka: a humanoid robot manipulation case study

Lin¹,

Christmann²,

Grando³

et al. 2019

The Knowledge Engineering Review

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To this day, manipulation still stands as one of the hardest challenges in robotics. In this work, we examine the board game Dr. Eureka as a benchmark to encourage further development in the field. The game consists of a race to solve a manipulation puzzle: reordering colored balls in transparent tubes, in which the solution requires planning, dexterity and agility. In this work, we present a robot (Tactical Hazardous Operations Robot 3) that can solve this problem, nicely integrating several classical and state-of-the-art techniques. We represent the puzzle states as graph and solve it as a shortest path problem, in addition to applying computer vision combined with precise motions to perform the manipulation. In this paper, we also present a customized implementation of YOLO (called YOLO-Dr. Eureka) and we implement an original neural network (NN)-based incremental solution to the inverse kinematics problem. We show that this NN outperforms the inverse of the Jacobian method for large step sizes. Albeit requiring more computation per control cycle, the larger steps allow for much larger movements per cycle. To evaluate the experiment, we perform trials against a human using the same set of initial conditions.

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Section: Introductionmentioning

confidence: 99%

Dr. Eureka: a humanoid robot manipulation case study

Lin¹,

Christmann²,

Grando³

et al. 2019

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…Instead, AI researchers found that the learned rules and hard coded knowledge developed in order to solve a specific task was not likely to be transferable to other tasks. The International Business Machines (IBM) Corporation was eventually able to develop a computer program which defeated the most highly ranked chess player in the world [2]. However, IBM's computer only accomplished one taskplaying chess.…”

Section: Introductionmentioning

confidence: 99%

Deep Blue Cannot Play Checkers: The Need for Generalized Intelligence for Mobile Robots

Kelley

Long

2010

Journal of Robotics

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Generalized intelligence is much more difficult than originally anticipated when Artificial Intelligence (AI) was first introduced in the early 1960s. Deep Blue, the chess playing supercomputer, was developed to defeat the top rated human chess player and successfully did so by defeating Gary Kasporov in 1997. However, Deep Blue only played chess; it did not play checkers, or any other games. Other examples of AI programs which learned and played games were successful at specific tasks, but generalizing the learned behavior to other domains was not attempted. So the question remains: Why is generalized intelligence so difficult? If complex tasks require a significant amount of development, time and task generalization is not easily accomplished, then a significant amount of effort is going to be required to develop an intelligent system. This approach will require a system of systems approach that uses many AI techniques: neural networks, fuzzy logic, and cognitive architectures.

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“…Timeline Showing Milestones in Algorithmic Development that Tackle Strategic-Planning Problems (Top) Key advances in algorithmic retrosynthetic planning for small organic molecules 1,5,[7][8][9][10]. (Bottom) Landmark examples from games where machine-learning algorithms have exceeded human capabilities [2][3][4].…”

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confidence: 99%

A Computer Conquers Tactical Combinations

Blair

Burke

2020

Chem

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Specialists in molecule making often employ the counterintuitive retrosynthetic strategy of initially increasing molecular complexity en route to simpler starting materials to produce synthetic sequences of unparalleled brevity. In this issue of Chem, Gryzbowski and co-workers describe how a computer algorithm can similarly identify these so-called ''tactical combinations'' and powerfully apply them to automatically identify highly efficient routes to complex molecular targets. Although computers can't yet outperform masters of synthetic organic chemistry, this breakthrough suggests that this day is coming.

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Man Versus Machine: Kasparov Versus Deep Blue

Cited by 15 publications

References 0 publications

Dr. Eureka: a humanoid robot manipulation case study

Dr. Eureka: a humanoid robot manipulation case study

Deep Blue Cannot Play Checkers: The Need for Generalized Intelligence for Mobile Robots

A Computer Conquers Tactical Combinations

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