A Comparative Study of Game Tree Searching Methods

Elnaggar, Ahmed A.; Aziem, Mostafa Abdel; Gadallah, Mahmoud; El-Deeb, Hesham

doi:10.14569/ijacsa.2014.050510

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Therefore, for better performance, it is attractive to use previous information that has already been got without searching again. Several most popular techniques based on this idea and discussed in this paper are iterative deepening, transposition table and refutation table, and killer heuristic and history heuristic [6].…”

Section: Enhancements Based On Results From Previous Searchingmentioning

confidence: 99%

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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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Section: Enhancements Based On Results From Previous Searchingmentioning

confidence: 99%

“…The core property of minimal window search [6] is that to prove a subtree inferior is faster than to confirm its exact score. It is based on the supposition that the move that is going to be searched is the best move and other moves are inferior.…”

Section: Minimal Window Searchmentioning

confidence: 99%

A study on search techniques in the game-tree

Zhang

2024

ACE

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“…To choose the best possible move, each of the two players (A and B) uses the MiniMax algorithm. The MiniMax algorithm is explained in (Elnaggar et al, 2014) and in (Kang et al, 2019). The Minimax algorithm is applied in two player deterministic games.…”

Section: Minimax Algorithmmentioning

confidence: 99%

GO Game Inspired Algorithm for Hardware Software Partitioning in Multiprocessor Embedded Systems

Iguider¹,

Bousselam²,

Elissati³

et al. 2019

CIS

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The codesign is a robust methodology, used in modern embedded systems with the objective of achieving the functional specifications and meeting the non-functional requirements. The most interesting step in the codesing is the process of Hardware/Software Partitioning. The aim is to decide which functionalities of the system should be implemented in hardware (HW) or in software (S W). In this article, a new heuristic algorithm is proposed to simultaneously optimize the hardware area (cost) and the execution time (performance) of a multiprocessor system. The proposed algorithm is inspired from game theory and especially from the GO game. The system is modeled using the DAG graph (Data Acyclic Graph), and two players (HW player and SW player) play in turn and choose a block (functionality) from the graph (system). The HW player has the goal of optimizing the global HW area while the SW player has the objective of minimizing the global execution time. After the game termination, and based on the 0-1 Knapsack algorithm, a step of refinement is used to meet the constraint on the total hardware area or on the overall execution time if a constraint is pre-defined. Experimental results show that the proposed algorithm gives better solutions compared to the Simulated Annealing algorithm and the Genetic Algorithm.

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“…Implementation of artificial intelligence and machine learning in mobile game applications requires utilizing the special search algorithms for optimize gaming decisions. Theoretical research in this field confirms relevance search optimization algorithms such as Minimax [1][2][3], Alpha-Beta Pruning [4], Greedy [5], PSO [6], suitable for such logic games.…”

Section: Introductionmentioning

confidence: 98%

Implementation of artificial intelligence in "ToqyzQumalaq" mobile logic game

Nurbekova,

Aimicheva,

Tolganbayuly

et al. 2024

SAPJ

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This research paper delves into the implementation of artificial intelligence (AI) in the mobile logic game "ToqyzQumalaq," focusing on incorporating advanced algorithmic strategies to improve gameplay. The game's complexity and strategic depth present unique challenges in AI development, addressed through the integration of algorithms like Minimax, Alpha-Beta Pruning, Greedy, and Particle Swarm Optimization (PSO). The study emphasizes the creation of evaluation functions for these algorithms, ensuring AI efficiency and human-like decision-making. This aspect is vital for maintaining the strategic unpredictability essential to "ToqyzQumalaq." Extensive experimental testing against human players of various skill levels demonstrates the algorithms' effectiveness. These tests reveal the strengths and limitations of each algorithm, providing insights into their application in the game. This paper contributes to AI in gaming, highlighting the challenges and opportunities in developing AI for complex games. Its findings are relevant not only to game developers but also serve as an educational tool, showcasing the practical application of AI and algorithmic strategies.

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A Comparative Study of Game Tree Searching Methods

Cited by 10 publications

References 14 publications

A study on search techniques in the game-tree

A study on search techniques in the game-tree

GO Game Inspired Algorithm for Hardware Software Partitioning in Multiprocessor Embedded Systems

Implementation of artificial intelligence in "ToqyzQumalaq" mobile logic game

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