A New Paradigm for Minimax Search
TLDR
This paper presents experimental data from three game-playing programs (checkers, Othello and chess), covering the range from low to high branching factor, and reports the first reported results that compare both depth-first and best-first algorithms given the same amount of memory.Abstract:
textThis paper introduces a new paradigm for minimax game-tree search algorithms. MT is a memory-enhanced version of Pearl's Test procedure. By changing the way MT is called, a number of best-first game-tree search algorithms can be simply and elegantly constructed (including SSS*).
Most of the assessments of minimax search algorithms have been based on simulations.
However, these simulations generally do not address two of the key ingredients of high
performance game-playing programs: iterative deepening and memory usage. This paper
presents experimental data from three game-playing programs (checkers, Othello and chess),
covering the range from low to high branching factor. The improved move ordering due to
iterative deepening and memory usage results in significantly different results from those
portrayed in the literature. Whereas some simulations show alpha-beta expanding almost
100% more leaf nodes than other algorithms [Marsland, Reinefeld & Schaeffer, 1987],
our results showed variations of less than 20%.
One new instance of our framework MTD(f) out-performs our best alpha-beta searcher
(aspiration NegaScout) on leaf nodes, total nodes and execution time. To our knowledge,
these are the first reported results that compare both depth-first and best-first algorithms given the same amount of memory.read more
Citations
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Journal ArticleDOI
Best-first fixed-depth minimax algorithms
TL;DR: A new formulation for Stockman's SSS ∗ algorithm, based on Alpha-Beta, is presented, finally transforming it into a practical algorithm, and a framework that facilitates the construction of several best-first fixed-depth game-tree search algorithms, known and new is presented.
Journal ArticleDOI
Teaching Artificial Intelligence and Logic Programming in a Competitive Environment
TL;DR: An experience in the context of undergraduate teaching of Artificial Intelligence at the Computer Science Department of the Faculty of Sciences in the University of Porto is described, where a sophisticated competition framework was developed to motivate students on the deepening of the topics covered in class.
Book
Learning to Play: Reinforcement Learning and Games
TL;DR: It is shown that the methods generalize to three games, hinting at artificial general intelligence, and an argument can be made that in doing so the authors failed the Turing test, since no human can play at this level.
Proceedings Article
Best-first fixed-depth game-tree search in practice
TL;DR: This work presents a new paradigm for minimax search algorithms: MT, a memory-enhanced version of Pearl's Test procedure, and reports the first reported results that compare both depth-first and best-first algorithms given the same amount of memory.
References
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Heuristics : Intelligent Search Strategies for Computer Problem Solving
TL;DR: This book presents, characterizes and analyzes problem solving strategies that are guided by heuristic information and provides examples of how these strategies have changed over time.
Journal ArticleDOI
An analysis of alpha-beta pruning
Donald E. Knuth,Ronald W. Moore +1 more
TL;DR: The alpha-beta procedure for searching game trees is shown to be optimal in a certain sense, and bounds are obtained for its running time with various kinds of random data.
Journal ArticleDOI
The history heuristic and alpha-beta search enhancements in practice
TL;DR: Results indicate that the history heuristic combined with transposition tables significantly outperforms other alpha-beta enhancements in application-generated game trees.
Journal ArticleDOI
The B* tree search algorithm: a best-first proof procedure
TL;DR: The algorithm, which is named B*, finds a proof that an arc at the root of a search tree is better than any other by attempting to find both the best arc atThe root and the simplest proof, in best-first fashion.
Journal ArticleDOI
A minimax algorithm better than alpha-beta?
TL;DR: An algorithm based on state space search is introduced for computing the minimax value of game trees and the new algorithm SSS∗ is shown to be more efficient than α-s in the sense that SSS ∗ never evaluates a node thatα-s can ignore.