Literature DB >> 17641166

Checkers is solved.

Jonathan Schaeffer1, Neil Burch, Yngvi Björnsson, Akihiro Kishimoto, Martin Müller, Robert Lake, Paul Lu, Steve Sutphen.   

Abstract

The game of checkers has roughly 500 billion billion possible positions (5 x 10(20)). The task of solving the game, determining the final result in a game with no mistakes made by either player, is daunting. Since 1989, almost continuously, dozens of computers have been working on solving checkers, applying state-of-the-art artificial intelligence techniques to the proving process. This paper announces that checkers is now solved: Perfect play by both sides leads to a draw. This is the most challenging popular game to be solved to date, roughly one million times as complex as Connect Four. Artificial intelligence technology has been used to generate strong heuristic-based game-playing programs, such as Deep Blue for chess. Solving a game takes this to the next level by replacing the heuristics with perfection.

Entities:  

Year:  2007        PMID: 17641166     DOI: 10.1126/science.1144079

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  9 in total

1.  Google AI algorithm masters ancient game of Go.

Authors:  Elizabeth Gibney
Journal:  Nature       Date:  2016-01-28       Impact factor: 49.962

Review 2.  Machine behaviour.

Authors:  Iyad Rahwan; Manuel Cebrian; Nick Obradovich; Josh Bongard; Jean-François Bonnefon; Cynthia Breazeal; Jacob W Crandall; Nicholas A Christakis; Iain D Couzin; Matthew O Jackson; Nicholas R Jennings; Ece Kamar; Isabel M Kloumann; Hugo Larochelle; David Lazer; Richard McElreath; Alan Mislove; David C Parkes; Alex 'Sandy' Pentland; Margaret E Roberts; Azim Shariff; Joshua B Tenenbaum; Michael Wellman
Journal:  Nature       Date:  2019-04-24       Impact factor: 49.962

3.  Response time distributions in rapid chess: a large-scale decision making experiment.

Authors:  Mariano Sigman; Pablo Etchemendy; Diego Fernández Slezak; Guillermo A Cecchi
Journal:  Front Neurosci       Date:  2010-10-07       Impact factor: 4.677

4.  Algorithmic and human prediction of success in human collaboration from visual features.

Authors:  Martin Saveski; Edmond Awad; Iyad Rahwan; Manuel Cebrian
Journal:  Sci Rep       Date:  2021-02-02       Impact factor: 4.379

5.  Confronting barriers to human-robot cooperation: balancing efficiency and risk in machine behavior.

Authors:  Tim Whiting; Alvika Gautam; Jacob Tye; Michael Simmons; Jordan Henstrom; Mayada Oudah; Jacob W Crandall
Journal:  iScience       Date:  2020-12-17

Review 6.  Recent Advances in General Game Playing.

Authors:  Maciej Świechowski; HyunSoo Park; Jacek Mańdziuk; Kyung-Joong Kim
Journal:  ScientificWorldJournal       Date:  2015-08-24

7.  Cooperating with machines.

Authors:  Jacob W Crandall; Mayada Oudah; Fatimah Ishowo-Oloko; Sherief Abdallah; Jean-François Bonnefon; Manuel Cebrian; Azim Shariff; Michael A Goodrich; Iyad Rahwan
Journal:  Nat Commun       Date:  2018-01-16       Impact factor: 14.919

8.  Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.

Authors:  Premanand Tiwari; Kathryn L Colborn; Derek E Smith; Fuyong Xing; Debashis Ghosh; Michael A Rosenberg
Journal:  JAMA Netw Open       Date:  2020-01-03

9.  Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists.

Authors:  Nader Chmait; Hans Westerbeek
Journal:  Front Sports Act Living       Date:  2021-12-08
  9 in total

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