The Analysis of Alpha Beta Pruning and MTD(f) Algorithm to Determine the Best Algorithm to be Implemented at Connect Four Prototype

Tommy, Lukas; Hardjianto, Mardi; Agani, Nazori

doi:10.1088/1757-899x/190/1/012044

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…MTD(f) is short for Memory-enhanced Test Drive [8]. Unlike PVS performs a full window search, MTD(f) completely uses null windows for searching, so it in many cases can outperform PVS.…”

Section: Minimal Window Searchmentioning

confidence: 99%

A study on search techniques in the game-tree

Zhang

2024

ACE

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Artificial intelligence has developed a lot in the game field and search techniques on game-trees are essential for AI-game-playing. As many techniques for searching the game-trees have been published, the time consumption of search has decreased and the accuracy of it has increased. This paper would provide a comprehensive review of existing algorithms and improvements, including their mechanisms and performances. These techniques are first divided into two sections, techniques based on Alpha-Beta pruning and techniques based on Monte-Carlo tree search. Furthermore, techniques based on Alpha-Beta pruning are further subdivided into narrow window properties and information from previous searches based on the specific aspects they focus on. Finally, this paper concludes by summarizing the performance of these techniques and identifying their suitable application scenarios, as well as suggesting potential directions for future research.

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“…MTD(f) is short for Memory-enhanced Test Drive [8]. Unlike PVS performs a full window search, MTD(f) completely uses null windows for searching, so it in many cases can outperform PVS.…”

Section: Minimal Window Searchmentioning

confidence: 99%

A study on search techniques in the game-tree

Zhang

2024

ACE

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“…The research idea was to reduce the created portion of game tree. Plamenka and Milena [8] in their research investigate the efficiency of parallel minimax algorithms for search in a game tree. Tic-Tac-Toe is used in their research to understand the efficiency [9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Minimax Algorithm Using Tic-Tac-Toe

Swaminathan¹,

Vaishali²,

Subashri³

2020

Intelligent Systems and Computer Technology

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The game industry has been on exponential growth, has different businesses of varying size, ethos, scope and beyond. Success of these video-games comes from a lot of labor-intensive work by developers. Every little nuance of each character, the objects within a character’s environment must be hand-coded. Repetitive work takes up a significant part of development time, which leads to an increase in glitches and logical flaws. Artificial intelligence has been used to simulate human players in software games, provides an opportunity for game developers to create unique experiences and different outcomes for each player. Computer chess players are well-known examples, wherein modern chess programs are trained to defeat best human players. AI based algorithms that can be implemented for games, but a need for optimal solutions is on a rise. We require a comparative analysis of multiple algorithms for understanding the most efficient and ideal one. In our work, through use of a game Tic-Tac-Toe various algorithms will be carried out with its prototype compared in terms of effective rate and optimality.

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Adversarial Search Algorithms Performance in the Yote Game

Torres

Barriga

2022

2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA)

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The Analysis of Alpha Beta Pruning and MTD(f) Algorithm to Determine the Best Algorithm to be Implemented at Connect Four Prototype

Cited by 3 publications

References 4 publications

A study on search techniques in the game-tree

A study on search techniques in the game-tree

Analysis of Minimax Algorithm Using Tic-Tac-Toe

Adversarial Search Algorithms Performance in the Yote Game

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