Designing an AI agent with the maximum possible win percentage is a complex task that involves various factors such as decision-making algorithms, learning techniques, and game-specific strategies. While I can't provide a specific paper or article for building an AI agent with the highest win percentage, I can provide guidance on the fundamental concepts and approaches involved.

1. Reinforcement Learning: Many successful AI game-playing agents are built using reinforcement learning techniques such as Q-learning, deep Q-networks (DQN), and policy gradient methods. Research papers on these topics can provide valuable insights.

2. Game Theory: Understanding the game's mechanics and employing game theory principles is crucial. Exploring academic articles or papers related to game theory and applications in AI can be beneficial.

3. Deep Learning: Utilizing deep neural networks for decision-making and strategy can significantly improve an AI agent's performance in games. Research papers focusing on deep learning in game AI can offer valuable knowledge.

4. Specific Game Strategies: If you have a particular game in mind, seeking articles or papers that discuss successful strategies for that game can provide applicable insights.

When building an AI agent with the highest possible win percentage, it's essential to consider these aspects and possibly explore academic databases, preprint repositories, or digital libraries for research papers and articles on these topics. Additionally, looking into recent conference proceedings or journals in the field of AI, machine learning, and game theory can also yield valuable resources.

Building an AI agent for Ultimate Tic Tac Toe involves several key steps to enhance its win percentage. Here's an outline of the process:

1. Understanding the Game: Ultimate Tic Tac Toe is an advanced version of the classic Tic Tac Toe, where the game is played on a larger 3x3 grid, and each cell of the grid contains a smaller 3x3 Tic Tac Toe board. The key is to understand the game rules and objectives thoroughly.

2. Game Representation: Represent the game state and actions in a way that facilitates decision-making. This can involve using a combination of data structures or frameworks to efficiently store and manipulate the game board and available moves.

3. Minimax Algorithm: Implement the Minimax algorithm with alpha-beta pruning to determine the best move for the AI agent. This algorithm involves recursively evaluating possible future game states to make optimal decisions.

4. Heuristic Evaluation Function: Develop a heuristic evaluation function to assess the desirability of a game state. This function should consider factors such as board control, repeated patterns, and potential winning configurations.

5. Monte Carlo Tree Search (MCTS): Consider using MCTS, a popular search algorithm for decision making in AI, to improve the agent's decision-making abilities. MCTS can effectively explore the game tree and select promising moves based on simulation results.

6. Deep Reinforcement Learning: Explore the application of deep reinforcement learning techniques, such as deep Q-networks (DQN) or policy gradient methods, to train the AI agent through self-play or supervised learning.

7. Testing and Optimization: Test the AI agent against a variety opponents, including human players and other AI agents. Continuously optimize the agent's strategies based on its performance.

To further enhance the AI agent's win percentage in Ultimate Tic Tac Toe, consider researching academic papers, articles, and existing implementations of AI agents for similar board games. Additionally, participating in AI competitions or forums focused on game AI development can provide valuable insights and strategies for improving the agent's performance.

Certainly! Here are a few articles and papers that could provide valuable insights into solving the problem of developing an AI agent for Ultimate Tic Tac Toe:

1. "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by Silver et al. (2017) - This paper introduces AlphaZero, a general reinforcement learning algorithm that achieved superhuman performance in Chess, Shogi, and Go. The techniques and principles outlined can be adapted for AI development in other strategic board games.

2. "Monte-Carlo Tree Search. In Handbook of Natural Computing" by Coulom, Rémi (2012) - This chapter provides an in-depth overview of Monte Carlo Tree Search (MCTS), explaining its principles and applications in game AI. Understanding MCTS can greatly benefit the development of AI agents for games like Ultimate Tic Tac Toe.

3. "Deep Learning for Tic-Tac-Toe with Keras" by Ahmed Gad (2019) - This article provides a practical example of using deep learning, specifically with Keras, to create a Tic Tac Toe AI agent. While not specific to Ultimate Tic Tac Toe, the concepts and approaches can be adapted to the more complex game.

4. "A Survey on Applications of Reinforcement Learning in Games" by Yuxi Li (2020) - This survey explores various applications of reinforcement learning in games, providing insights into the state-of-the-art approaches for developing AI agents for board games and video games.

5. "Playing Atari with Deep Reinforcement Learning" by Mnih et al. (2013) - This seminal paper introduces the concept of deep Q-networks (DQN) and their application to learning to play Atari games. While not directly related to Tic Tac Toe, the principles can be informative for developing AI agents in strategic board games.

Exploring these articles and papers can valuable knowledge and strategies for developing a high-performing AI agent for Ultimate Tic Tac Toe. Additionally, searching academic databases and digital libraries for related research in the fields of game AI, reinforcement learning, and board game strategies may yield additional valuable resources.