Role of Particle Swarm Optimization in Computer Games

Singh, Gagandeep; Deep, Kusum

doi:10.1007/978-81-322-2220-0_21

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…An important step in implementation search algorithms for optimize gaming decisions is in the building of the evaluation function of the game decision tree [4,5,6].…”

Section: Game Mechanics Of the Mobile Logic Game "Toqyzqumalaq"mentioning

confidence: 99%

“…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%

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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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“…An important step in implementation search algorithms for optimize gaming decisions is in the building of the evaluation function of the game decision tree [4,5,6].…”

Section: Game Mechanics Of the Mobile Logic Game "Toqyzqumalaq"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Implementation of artificial intelligence in "ToqyzQumalaq" mobile logic game

Nurbekova,

Aimicheva,

Tolganbayuly

et al. 2024

SAPJ

View full text Add to dashboard Cite

show abstract

“…From optimization applications point of view, the results obtained in this paper show that RSS has the potentials to be used in solving problems such as cancer classification applications[ 82 ], optimization of web service composition processes [ 83 ], vehicle routing system applications [ 84 ], design of embedded systems [ 85 ], collective robotic search applications [ 86 ], data clustering applications [ 87 – 90 ], digital games applications [ 91 ], medical images applications [ 92 ], etc.…”

Section: Significancementioning

confidence: 99%

Ringed Seal Search for Global Optimization via a Sensitive Search Model

et al. 2016

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The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems.

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Role of Particle Swarm Optimization in Computer Games

Cited by 2 publications

References 28 publications

Implementation of artificial intelligence in "ToqyzQumalaq" mobile logic game

Implementation of artificial intelligence in "ToqyzQumalaq" mobile logic game

Ringed Seal Search for Global Optimization via a Sensitive Search Model

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