A New Paradigm for Minimax Search
This work addresses the gap in assessing minimax search algorithms for high-performance game-playing programs by considering iterative deepening and memory usage, offering a more accurate comparison for researchers and developers in AI gaming.
The paper tackles the problem of evaluating minimax game-tree search algorithms by introducing a new paradigm, MT, and providing experimental data from checkers, Othello, and chess programs, showing that variations in leaf node expansions are less than 20% compared to literature claims of 100% more, with MTD-f outperforming the best alpha-beta searcher in leaf nodes, total nodes, and execution time.
This paper introduces a new paradigm for minimax game-tree search algo- rithms. MT is a memory-enhanced version of Pearls 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 [12], 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