{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-new-paradigm-for-minimax-search","title":"A New Paradigm for Minimax Search","arxiv_id":"1404.1515","date":"2014-04-05","proceeding":null,"authors":["Aske Plaat","Jonathan Schaeffer","Wim Pijls","Arie de Bruin"],"abstract":"This paper introduces a new paradigm for minimax game-tree search algo-\nrithms. MT is a memory-enhanced version of Pearls Test procedure. By changing\nthe way MT is called, a number of best-first game-tree search algorithms can be\nsimply and elegantly constructed (including SSS*). Most of the assessments of\nminimax search algorithms have been based on simulations. However, these\nsimulations generally do not address two of the key ingredients of high\nperformance game-playing programs: iterative deepening and memory usage. This\npaper presents experimental data from three game-playing programs (checkers,\nOthello and chess), covering the range from low to high branching factor. The\nimproved move ordering due to iterative deepening and memory usage results in\nsignificantly different results from those portrayed in the literature. Whereas\nsome simulations show Alpha-Beta expanding almost 100% more leaf nodes than\nother algorithms [12], our results showed variations of less than 20%. One new\ninstance of our framework (MTD-f) out-performs our best alpha- beta searcher\n(aspiration NegaScout) on leaf nodes, total nodes and execution time. To our\nknowledge, these are the first reported results that compare both depth-first\nand best-first algorithms given the same amount of memory","url_abs":"http://arxiv.org/abs/1404.1515v1","url_pdf":"http://arxiv.org/pdf/1404.1515v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-new-paradigm-for-minimax-search","repo_url":"https://github.com/bewrrrie/ChessAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}