AI-Driven Optimization: Enhancing 2048 Strategies with Monte Carlo Tree Search

AI-Driven Optimization: Enhancing 2048 Strategies with Monte Carlo Tree Search.

[Audio] I will begin by introducing AI-driven optimization and providing an overview of our related work. Following this, I will discuss our methodology for utilizing Monte Carlo Tree Search to optimize 2048 strategies. Subsequently, I will present our results and evaluate the effectiveness of our approach. Lastly, I will discuss the implications of our research and provide a conclusion..

[Audio] AI is revolutionizing the world, and games provide an intriguing platform to assess the capabilities of this technology. We undertook research into the use of AI to optimize strategies in the game 2048. We created a custom Monte Carlo Tree Search algorithm to outplay traditional methods, allowing for more accurate predictions of how each move affects the score outcome. Testing revealed our method to outperform existing benchmarks, yielding higher average scores and attaining better maximum tile achievements. Such results inherently hint at the potential to enhance the way games are designed and played, introducing users to never before seen strategies. Our research may well be a milestone in the history of both AI and interactive entertainment..

[Audio] Computing power and technology are advancing, expanding the scope of artificial intelligence in gaming. Our research focuses on constructing an AI-driven optimisation of 2048 strategies. MCTS is one of the most prominent algorithms used in AI advancement. This technique uses simulations to evaluate potential game states and determine the best moves. Our 2048 AI is based on the AlphaGo Zero's training strategy, using simulations to refine its decision-making. This highly developed combination of algorithms and simulations delivers an improved playing experience for competitive 2048 players..

[Audio] Discussing AI-Driven Optimization and how it can be used to enhance 2048 Strategies with Monte Carlo Tree Search is the agenda. We modeled 2048 as a Markov Decision Process, or MDP, with a 4x4 grid. This enabled us to create a start_game() function to generate a blank grid and place two tiles randomly. Core move functions like shift_grid_right() and merge_tiles() were then developed to move elements and merge adjacent equal tiles respectively. Consequently, this enabled the AI decision making with MCTS, by computing search params and making the AI move with game mechanics, structure and Monte Carlo Tree Search. As a result, optimizing 2048 Strategies was possible, leading the game to the next level..

[Audio] Monte Carlo Tree Search is a powerful technique used to enhance strategies for the popular 2048 game. It involves a four-step process of Selection, Expansion, Simulation, and Backpropagation to evaluate the possible moves and outcomes of the game. An Upper Confidence Bound formula is incorporated to guarantee appropriate exploration and exploitation. An exploration parameter is also adjusted to set the value of unexplored actions. This Monte Carlo Tree Search technique further optimizes and improves the strategies for 2048..

7. RESULTS EVALUATION. Performance by Max Score: The agent performed best in achieving a score of 1024, followed by 512 and 2048. It rarely reached lower scores. Time Complexity: The average time taken to reach different maximum scores: The average time taken to reach different maximum scores exhibits an increasing trend with higher scores. Games with a maximum score of 2048 took the longest on average, followed by 1024, 512, and 256, while the game with a maximum score of 128 was the quickest. Overall Performance: The agent demonstrated an ability to reach high scores but with varying time requirements, highlighting its efficiency in achieving different milestones in the game..

[Audio] MCTS has been shown to be effective in navigating the complexities of the 2048 puzzle, outperforming a random model. This suggests the potential of MCTS for allocating resources and making decisions across many domains. AI has further shown potential to contribute to problem-solving tasks other than in gaming scenarios, indicating wider applications than initially thought..

[Audio] AI has drastically transformed the gaming industry, providing a great capacity for optimizing the gaming experience for both game developers and gamers. Monte Carlo Tree Search (MCTS) is an AI-driven optimization algorithm, providing the opportunity to create complex strategies and automatically customize them to the game, leading to improved decision-making and performance. It also offers a stimulating and immersive experience for players. However, introducing AI into gaming raises a variety of ethical issues, such as guarding against AI taking precedence over human creativity and preserving the uniqueness of the gaming experience. We must also contemplate the difficulties of utilizing AI in games with numerous potential paths and how to best apply existing knowledge in an organized way. To take full advantage of AI-driven gaming, we need to look ahead to options such as a variety of AI adversaries, multiplayer gaming elements, and contemporary display solutions. By exploiting AI technology, we can create incredibly intelligent gaming experiences..

[Audio] AI capabilities are rapidly evolving, with Monte Carlo Tree Search (MCTS) leading the way in tackling difficult tasks. Even the random models are outrun by MCTS in the game 2048. There is potential to further develop MCTS through parameter tuning and heuristic exploration to enhance the results. These same approaches can be applied in many contexts other than gaming, including strategic games, resource allocation, and decision-making scenarios. AI is proving to be a promising technology offering feasible solutions to many of our biggest issues..

[Audio] AI-Driven Optimization has proven to be an effective mechanism in enhancing 2048 strategies with Monte Carlo Tree Search. By merging this approach, it is now possible to uncover quicker and more efficient methods to finish the game states. Shivani Gattigorla, Student ID: D3622034 carried out this project, as shown on the slide. If you have any questions, please do not hesitate to contact me. Appreciate your attention and I trust this presentation was of use to you..