Michael Bowling has always loved games. When he was growing up in Ohio, his parents were avid card players, dealing out hands of everything from euchre to gin rummy. Meanwhile, he and his friends would tear up board games lying around the family home and combine the pieces to make their own games, with new challenges and new markers for victory. Bowling has come far from his days of playing with colourful cards and plastic dice. He has three degrees in computing science and is now a professor at the University of Alberta.
In 1952, IBMer Arthur Samuel created the first implementation of a machine learning system in America -- to play checkers. At first, the system was beatable. Samuel continued to improve the learning capabilities of his checkers program, and in part trained the program by having it play thousands of games against itself. By 1961, Samuel's programs played the fourth-ranked checkers player in America and won. This demonstrated a level of play not yet achieved by a computer.
In 1952, IBMer Arthur Samuel created the first implementation of a machine learning system in America -- to play checkers. At first, the system was beatable. Samuel continued to improve the learning capabilities of his checkers program, and in part trained the program by having it play thousands of games against itself. By 1961, Samuel's programs played the fourth-ranked checkers player ... Read More
In this episode of Robots in Depth, Per Sjöborg speaks with Peter Corke, distinguished professor of robotic vision from Queensland University of Technology, and Director of the ARC Centre of Excellence for Robotic Vision. Peter is well known for his work in computer vision and has written one of the books that defines the area. He talks about how serendipity made him build a checkers playing robot and then move on to robotics and machine vision. We get to hear about how early experiments with "Blob Vision" got him interested in analyzing images and especially moving images, and his long and interesting journey giving robots eyes to see the world.
The idea that machines can make intelligent decisions has been around since the 1950s when the first learning programme was built. At the time, the machine itself was groundbreaking, improving at the game of checkers the more it played. Since then, the idea of such machines has become more and more prevalent, particularly in pop culture.
CHINOOK that I also highly recommend. AI Magazine Volume 20 Number 1 (1999) ( AAAI) vided more than a glimpse of the intense process it described. One Jump Ahead was written by the person most involved in the process. Thus, it provides us with a direct view of Schaeffer's maturation--a maturation that we should all hope to have. Schaeffer does not pull any punches in his book; we see many of his elations, his disappointments, and his flaws.
Samuel's successes included a victory by his program over a master-level player. In fact, the opponent was not a master, and Samuel himself had no illusions about his program's strength. This single event, a milestone in AI, was magnified out of proportion by the media and helped to create the impression that checkers was a solved game. Nevertheless, his work stands as a major achievement in machine learning and AI. Since 1950, the checkers world has been dominated by Tinsley.
Arthur Samuel (1901-1990) was a pioneer of artificial intelligence research. From 1949 through the late 1960s, he did the best work in making computers learn from their experience. His vehicle for this work was the game of checkers. Programs for playing games often fill the role in artificial intelligence research that the fruit fly Drosophila plays in genetics. Drosophilae are convenient for genetics because they breed fast and are cheap to keep, and games are convenient for artificial intelligence because it is easy to compare a computer's performance on games with that of a person.