The winner decision model of tic tac toe game by using multi-tape turing machine

Garg, Sneha; Songara, Dalpat

doi:10.1109/icacci.2016.7732107

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Karamchandani et al [47] discussed the techniques used in [44][45] and presented a very basic rule-based algorithm, which always targets the centre at the start and has a set of special moves, but it can still be beaten by an opponent in some cases. Sneha Garg et al [48][49] formally defined the Tic Tac Toe problem and formulated a winning strategy for the same using a multitape Turing machine and automata theory.…”

Section: Literature Reviewmentioning

confidence: 99%

Randomised fast no‐loss expert system to play tic‐tac‐toe like a human

Paul

2020

Cognitive Computation and Systems

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This paper introduces a blazingly fast, no-loss expert system for Tic Tac Toe using Decision Trees called T3DT, that 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. In order to make the gameplay more human-like, randomization is prioritized 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 paper. T3DT also doesn't need the data sets or the time to train an evolutionary model, making it a practical no-loss approach to play Tic Tac Toe.

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Section: Literature Reviewmentioning

confidence: 99%

Randomised fast no‐loss expert system to play tic‐tac‐toe like a human

Paul

2020

Cognitive Computation and Systems

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“…Similarly, Ref. [14] used one of the concepts of automata theory, which is the Multi-tape Turing Machine algorithm, to solve the tic-tac-toe game with optimal results.…”

Section: Introductionmentioning

confidence: 99%

Playing the SOS Game Using Feasible Greedy Strategy

Setiawan

2020

CommIT (Communication and Information Technology) Journal

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The research aims to make an intelligent agent that can compete against the human player. In this research, the feasible greedy strategy is proposed to make an intelligent agent by checking all possible solutions in the limited tree levels to find effective movement. Several matches are conducted to evaluate the performance of the feasible greedy agent. The board size for the evaluation consists of 33, 44, 55, 66, 77, and 88 squares. From the result, the feasible greedy agent never loses against the random agent and the pure greedy agent. In 3 3 squares match, the agent can compensate against the human player, so the game always ends with a draw. In 44, 55, 66, 77, and 88 squares matches, the feasible greedy agent slightly outplays the human player.

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