Adaptive Snake Game with AI Control Article Swipe
Diksha Waghmare
,
Alisha Mulani
,
S. R. Takale
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48175/ijarsct-29523
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48175/ijarsct-29523
By fusing AI-controlled and manual gameplay, Advanced Snake, a sophisticated browser-based game, was developed to enhance the classic Snake experience. In classic snake games, a growing snake must be steered across a limited grid without colliding with itself or the walls. This project preserves the basic fundamentals of the original game while adding some innovative components to make it more engaging and educational. Players can choose between two major modes of play: player-controlled mode, where they move the snake using keyboard inputs (arrow keys or WASD), and AI-controlled mode, which employs the A* pathfinding algorithm to automatically determine the optimal way to acquire food while avoiding obstacles. This AI setup allows users to observe the operation of pathfinding and collision avoidance algorithms in a dynamic, real-time environment. In order to demonstrate fundamental concepts of artificial intelligence decision-making, the AI continuously recalculates the snake's path as the game board and its position change. In order to enhance the user experience, the project includes variable pace settings that let players change the game's tempo to fit their ability level. Different visual skins (Classic, Neon, and Retro) can be applied to alter the appearance of the game grid and snake. To further aid AI or players in visualizing the game environment, a toggleable grid overlay can be activated. Using a real-time difficulty scaling algorithm, the adaptive component of the game adjusts the playing environment based on the performance of the AI or player. The snake's speed can be increased, new obstacles can be added, the allowed space can be reduced, or the scoring method can be changed. In addition to guaranteeing that the game stays difficult, this method offers a great platform for testing the resilience and learning potential of various AI techniques. According to the experimental findings, heuristic algorithms do well in environments that are static or semi-predictable but suffer as complexity rises. Strong path optimization is shown by A* search, however it is not flexible enough to adjust to dynamic elements like shifting barriers or changing game speed. However, when trained in a suitably diversified context, agents based on reinforcement learning exhibit encouraging outcomes. In addition to learning intricate tactics like looping safely, avoiding traps, and optimizing reward collecting, agents trained with deep Q-networks (DQN) exhibit the capacity to generalize across levels. Large amounts of processing power and meticulous adjustment of hyperparameters like learning rate, reward structure, and exploration-exploitation balance are necessary for training such agents.
Related Topics
Concepts
Computer science
Grid
Pathfinding
Artificial intelligence
Path (computing)
Pace
Human–computer interaction
Component (thermodynamics)
Position (finance)
Overlay
Control (management)
Video game
Variable (mathematics)
Simulation
Orientation (vector space)
Collision avoidance
Order (exchange)
Computer vision
Space (punctuation)
Video game development
Computer game
Robot
Key (lock)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.48175/ijarsct-29523
- https://doi.org/10.48175/ijarsct-29523
- OA Status
- diamond
- References
- 88
- OpenAlex ID
- https://openalex.org/W7105841903
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105841903Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48175/ijarsct-29523Digital Object Identifier
- Title
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Adaptive Snake Game with AI ControlWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-11-17Full publication date if available
- Authors
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Diksha Waghmare, Alisha Mulani, S. R. TakaleList of authors in order
- Landing page
-
https://doi.org/10.48175/ijarsct-29523Publisher landing page
- PDF URL
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https://doi.org/10.48175/ijarsct-29523Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.48175/ijarsct-29523Direct OA link when available
- Concepts
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Computer science, Grid, Pathfinding, Artificial intelligence, Path (computing), Pace, Human–computer interaction, Component (thermodynamics), Position (finance), Overlay, Control (management), Video game, Variable (mathematics), Simulation, Orientation (vector space), Collision avoidance, Order (exchange), Computer vision, Space (punctuation), Video game development, Computer game, Robot, Key (lock)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
88Number of works referenced by this work
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| publication_date | 2025-11-17 |
| publication_year | 2025 |
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| referenced_works_count | 88 |
| abstract_inverted_index.a | 9, 25, 32, 124, 209, 217, 277, 342 |
| abstract_inverted_index.A* | 93, 318 |
| abstract_inverted_index.AI | 109, 139, 201, 238, 289 |
| abstract_inverted_index.By | 1 |
| abstract_inverted_index.In | 21, 128, 153, 265, 354 |
| abstract_inverted_index.To | 198 |
| abstract_inverted_index.as | 145, 309 |
| abstract_inverted_index.be | 29, 186, 214, 245, 250, 256, 263 |
| abstract_inverted_index.by | 317 |
| abstract_inverted_index.do | 298 |
| abstract_inverted_index.in | 123, 204, 300, 341 |
| abstract_inverted_index.is | 315, 322 |
| abstract_inverted_index.it | 59, 321 |
| abstract_inverted_index.of | 48, 71, 117, 134, 192, 225, 236, 287, 384, 390 |
| abstract_inverted_index.on | 233, 348 |
| abstract_inverted_index.or | 39, 85, 202, 239, 258, 305, 334 |
| abstract_inverted_index.to | 15, 57, 96, 102, 113, 130, 155, 173, 188, 267, 292, 326, 328, 356, 378 |
| abstract_inverted_index.The | 241 |
| abstract_inverted_index.aid | 200 |
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| abstract_inverted_index.for | 280, 402 |
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| abstract_inverted_index.let | 167 |
| abstract_inverted_index.new | 247 |
| abstract_inverted_index.not | 323 |
| abstract_inverted_index.the | 17, 40, 45, 49, 78, 92, 99, 115, 138, 142, 146, 157, 160, 170, 190, 193, 206, 222, 226, 229, 234, 237, 252, 259, 270, 282, 293, 376 |
| abstract_inverted_index.two | 68 |
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| abstract_inverted_index.way | 101 |
| abstract_inverted_index.This | 42, 108 |
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| abstract_inverted_index.food | 104 |
| abstract_inverted_index.game | 51, 147, 194, 207, 227, 271, 336 |
| abstract_inverted_index.grid | 34, 195, 211 |
| abstract_inverted_index.keys | 84 |
| abstract_inverted_index.like | 331, 360, 392 |
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| abstract_inverted_index.pace | 164 |
| abstract_inverted_index.path | 144, 313 |
| abstract_inverted_index.some | 54 |
| abstract_inverted_index.such | 404 |
| abstract_inverted_index.that | 166, 269, 302 |
| abstract_inverted_index.they | 76 |
| abstract_inverted_index.this | 274 |
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| abstract_inverted_index.well | 299 |
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| abstract_inverted_index.with | 37, 371 |
| abstract_inverted_index.(DQN) | 374 |
| abstract_inverted_index.Large | 382 |
| abstract_inverted_index.Neon, | 182 |
| abstract_inverted_index.Snake | 19 |
| abstract_inverted_index.Using | 216 |
| abstract_inverted_index.alter | 189 |
| abstract_inverted_index.based | 232, 347 |
| abstract_inverted_index.basic | 46 |
| abstract_inverted_index.board | 148 |
| abstract_inverted_index.game, | 12 |
| abstract_inverted_index.great | 278 |
| abstract_inverted_index.major | 69 |
| abstract_inverted_index.mode, | 74, 89 |
| abstract_inverted_index.modes | 70 |
| abstract_inverted_index.order | 129, 154 |
| abstract_inverted_index.play: | 72 |
| abstract_inverted_index.power | 386 |
| abstract_inverted_index.rate, | 394 |
| abstract_inverted_index.setup | 110 |
| abstract_inverted_index.shown | 316 |
| abstract_inverted_index.skins | 180 |
| abstract_inverted_index.snake | 23, 27, 79 |
| abstract_inverted_index.space | 254 |
| abstract_inverted_index.speed | 243 |
| abstract_inverted_index.stays | 272 |
| abstract_inverted_index.tempo | 172 |
| abstract_inverted_index.their | 175 |
| abstract_inverted_index.users | 112 |
| abstract_inverted_index.using | 80 |
| abstract_inverted_index.where | 75 |
| abstract_inverted_index.which | 90 |
| abstract_inverted_index.while | 52, 105 |
| abstract_inverted_index.(arrow | 83 |
| abstract_inverted_index.Retro) | 184 |
| abstract_inverted_index.Snake, | 8 |
| abstract_inverted_index.Strong | 312 |
| abstract_inverted_index.WASD), | 86 |
| abstract_inverted_index.across | 31, 380 |
| abstract_inverted_index.added, | 251 |
| abstract_inverted_index.adding | 53 |
| abstract_inverted_index.adjust | 327 |
| abstract_inverted_index.agents | 346, 369 |
| abstract_inverted_index.allows | 111 |
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| abstract_inverted_index.choose | 66 |
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| abstract_inverted_index.game's | 171 |
| abstract_inverted_index.games, | 24 |
| abstract_inverted_index.inputs | 82 |
| abstract_inverted_index.itself | 38 |
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| abstract_inverted_index.manual | 5 |
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| abstract_inverted_index.reward | 367, 395 |
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| abstract_inverted_index.traps, | 364 |
| abstract_inverted_index.visual | 179 |
| abstract_inverted_index.walls. | 41 |
| abstract_inverted_index.Players | 64 |
| abstract_inverted_index.ability | 176 |
| abstract_inverted_index.acquire | 103 |
| abstract_inverted_index.adjusts | 228 |
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| abstract_inverted_index.enhance | 16, 156 |
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| abstract_inverted_index.however | 320 |
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| abstract_inverted_index.players | 168, 203 |
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| abstract_inverted_index.snake's | 143, 242 |
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| abstract_inverted_index.Different | 178 |
| abstract_inverted_index.algorithm | 95 |
| abstract_inverted_index.avoidance | 121 |
| abstract_inverted_index.colliding | 36 |
| abstract_inverted_index.collision | 120 |
| abstract_inverted_index.component | 224 |
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| abstract_inverted_index.findings, | 295 |
| abstract_inverted_index.gameplay, | 6 |
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| abstract_inverted_index.necessary | 401 |
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| abstract_inverted_index.outcomes. | 353 |
| abstract_inverted_index.potential | 286 |
| abstract_inverted_index.preserves | 44 |
| abstract_inverted_index.real-time | 126, 218 |
| abstract_inverted_index.Q-networks | 373 |
| abstract_inverted_index.activated. | 215 |
| abstract_inverted_index.adjustment | 389 |
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| abstract_inverted_index.algorithms | 122, 297 |
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| abstract_inverted_index.artificial | 135 |
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| abstract_inverted_index.obstacles. | 107 |
| abstract_inverted_index.optimizing | 366 |
| abstract_inverted_index.processing | 385 |
| abstract_inverted_index.resilience | 283 |
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| abstract_inverted_index.sophisticated | 10 |
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| abstract_inverted_index.semi-predictable | 306 |
| abstract_inverted_index.player-controlled | 73 |
| abstract_inverted_index.exploration-exploitation | 398 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.83552564 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |