Randomised fast no‐loss expert system to play tic‐tac‐toe like a human. Issue 4 (9th November 2020)

• Record Type: Journal Article
• Authors: Paul, Aditya Jyoti
• Is Part Of: Cognitive computation and systems. Volume 2:Issue 4(2020)
• Page Start: 231
• Page End: 241
• Publication Date: 2020-11-09
• Subjects: trees (mathematics) -- minimax techniques -- tree searching -- game theory -- computer games -- decision trees -- learning (artificial intelligence) -- evolutionary computation; no‐loss expert system -- tic‐tac‐toe -- decision trees -- T3DT -- human gameplay -- brute force -- randomisation -- complete game tree -- practical no‐loss approach; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2020.0018
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : This study introduces a blazingly fast, no‐loss expert system for tic‐tac‐toe using decision trees called T3DT, which tries to emulate human gameplay as closely as possible. It does not make use of any brute force, minimax, or evolutionary techniques, but is still always unbeatable. To make the gameplay more human‐like, randomisation is prioritised and T3DT randomly chooses one of the multiple optimal moves at each step. Since it does not need to analyse the complete game tree at any point, T3DT is exceptionally faster than any brute force or minimax algorithm, this has been shown theoretically as well as empirically from clock‐time analyses in this study. T3DT also does not need the data sets or the time to train an evolutionary model, making it a practical no‐loss approach to play tic‐tac‐toe.