Some studies in machine learning using the game of checkers
Abstract
Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program. Furthermore, it can learn to do this in a remarkably short period of time (8 or 10 hours of machine-playing time) when given only the rules of the game, a sense of direction, and a redundant and incomplete list of parameters which are thought to have something to do with the game, but whose correct signs and relative weights are unknown and unspecified. The principles of machine learning verified by these experiments are, of course, applicable to many other situations.
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Rule-based systems are expensive to build and brittle, and it is challenging to encode higher-order interactions between different pieces of knowledge provided by different specialists, with system effectiveness restricted by comprehensiveness of pre-existing medical knowledge.
“However, rule-based systems are expensive to build and brittle. Furthermore, it is challenging to encode higher-order interactions between different pieces of knowledge provided by different specialists, with system effectiveness restricted by the comprehensiveness of pre-existing medical knowledge.12”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [12]
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References 9 · Cited by 1,439
- Authors
- Samuel, A. L.
- Corresponding author
- Samuel, A. L.
- Venue
- IBM Journal of Research and Development
- Publisher
- IBM
- Published
- 2000-01
- Volume
- 44
- Issue
- 1.2
- Pages
- 206-226
- ISSN
- 0018-8646 (ISSN-L)
- OpenAlex
- W2138178898
- Cited by
- 1,439
- References
- 9
- Citations by year
- 2026: 35 · 2025: 56 · 2024: 94 · 2023: 137 · 2022: 86 · 2021: 124 · 2020: 94 · 2019: 78 · 2018: 60 · 2017: 16 · 2016: 16 · 2015: 18 · 2014: 29 · 2013: 27 · 2012: 21
- FWCI
- 41.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IBM Journal of Research and Development (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- machine learning, checkers, video gaming, reinforcement learning, game-playing programs
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