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Bootstrapping from Game Tree Search

Neural Information Processing Systems

In this paper we introduce a new algorithm for updating the parameters of a heuristic evaluation function, by updating the heuristic towards the values computed by an alpha-beta search. Our algorithm differs from previous approaches to learning from search, such as Samuels checkers player and the TD-Leaf algorithm, in two key ways. First, we update all nodes in the search tree, rather than a single node. Second, we use the outcome of a deep search, instead of the outcome of a subsequent search, as the training signal for the evaluation function. We implemented our algorithm in a chess program Meep, using a linear heuristic function.


Standing on the shoulders of giants

#artificialintelligence

When you think of AI or machine learning you may draw up images of AlphaZero or even some science fiction reference such as HAL-9000 from 2001: A Space Odyssey. However, the true forefather, who set the stage for all of this, was the great Arthur Samuel. Samuel was a computer scientist, visionary, and pioneer, who wrote the first checkers program for the IBM 701 in the early 1950s. His program, "Samuel's Checkers Program", was first shown to the general public on TV on February 24th, 1956, and the impact was so powerful that IBM stock went up 15 points overnight (a huge jump at that time). This program also helped set the stage for all the modern chess programs we have come to know so well, with features like look-ahead, an evaluation function, and a mini-max search that he would later develop into alpha-beta pruning.


1343

AI Magazine

AI Game-Playing Techniques: Are They Useful for Anything Other Than Games? In conjunction with the American Association for Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research. AAAI-98's Hall of Champions exhibit) is an AI games researcher at the University of Alberta and author of the checkers program The early research on the alpha-beta search algorithm was useful in establishing a foundation for AI theories of heuristic search, and these theories have been useful in many areas of AI. Several of the panelists (particularly Schaeffer, Wilkins, and Fotland) pointed out that the minimax search algorithms traditionally associated with AI have only a limited range of applicability.


Checkers computer becomes invincible

AITopics Original Links

An invincible checkers-playing program named Chinook has solved a game whose origins date back several millennia, scientists reported Thursday on the journal Science's Web site. By playing out every possible move -- about 500 billion billion in all -- the computer proved it can never be beaten. Even if its opponent also played flawlessly, the outcome would be a draw. Chinook, created by computer scientists from the University of Alberta in 1989, wrapped up its work less than three months ago. In doing so, its programmers say the newly crowned checkers king has solved the most challenging game yet cracked by a machine -- even outdoing the chess-playing wizardry of IBM's Deep Blue.


A 'Brief' History of Game AI Up To AlphaGo, Part 2

#artificialintelligence

This is the second part of'A Brief History of Game AI Up to AlphaGo'. Part 1 is here and part 3 is here.


Computers, Chess, and Cognition

Classics

The checkers program Chinook has won the right to play a 40-game match for the World Checkers Championship against Dr. Marion Tinsley. This was earned by placing second, after Dr. Tinsley, at the 1990 U.S. National Open, the biennial event used to determine a challenger for the championship. This is the first time a program has earned the right to contest for a human world championship. In an exhibition match played in December 1990, Tinsley narrowly defeated Chinook 7.5-6.5. This paper describes the program, the research problems encountered and our solutions.