access icon openaccess Randomised fast no-loss expert system to play tic-tac-toe like a human

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.

Inspec keywords: evolutionary computation; learning (artificial intelligence); trees (mathematics); computer games; minimax techniques; decision trees; game theory; tree searching

Other keywords: tic-tac-toe; practical no-loss approach; brute force; randomisation; human gameplay; decision trees; complete game tree; no-loss expert system; T3DT

Subjects: Optimisation techniques; Game theory; Computer games; Combinatorial mathematics; Optimisation techniques; Knowledge engineering techniques

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