Artificial Intelligence in Game Strategies

The transformative power of artificial intelligence (AI) extends far beyond conventional applications, making a particularly profound impact in the realm of gaming. As this technology evolves, it continues to reshape how games are developed and experienced, elevating player immersion through enhanced realism, deeper engagement, and more sophisticated challenges. Perhaps the most striking demonstration of AI's capabilities in gaming lies in its mastery of strategic board games - from Chess to Go to Checkers - where AI systems have achieved performance levels that surpass human expertise in ways previously thought impossible.

1.  Google DeepMind’s systems forChess and Go

In 2017, Google’s artificial intelligence company DeepMind introduced AlphaZero, an AI system that could teach itself how to play chess, shogi, and Go. This was a major breakthrough in the field of artificial intelligence, as it demonstrated that an AI system could teach itself how to play a complex game without human intervention. AlphaZero used a combination of reinforcement learning, neural networks, and Monte Carlo tree search to teach itself how to play chess at a level that surpassed that of the best chess engines at the time.

In 2016, the computer program AlphaGo captured the world’s attention when it defeated the legendaryGo player Lee Sedol. The ancient board game of Go is one of the most complex games ever devised, with more possible board configurations than atoms in the universe. It was a longstanding grand challenge for artificial intelligence andAlphaGo’s 4-1 win was considered by many to be a decade ahead of its time. The system was invented by DeepMind, co-founded by scientist Demis Hassabis. Five months earlier, AlphaGo had beaten European champion Fan Hui, becoming the first program to defeat a professional player.

These achievements have advanced AI and inspired human players to adopt strategies to improve their skills.

2.  INRIA on poker-playing algorithms

While traditional boardgames like chess, draughts, and go operate on the principle of perfect information shared equally between players, they fail to capture a crucial aspect of real-world decision-making: information asymmetry. This is where poker emerges as a uniquely fascinating domain for AI research, as it mirrors the reality of decision-making under uncertainty with hidden information. In a breakthrough achievement in 2021, researchers collaborating with INRIA'sFAIRPLAY team developed sophisticated poker-playing algorithms that addressed this complexity. Their innovative approach focused on achieving optimal solutions within practical time constraints. Through the doctoral research of Côme Fiegel, supported by the FAIRPLAY team, these algorithms were refined to rapidly learn near-optimal strategies, marking a significant advancement inAI's ability to handle scenarios with incomplete information.

In the gaming realm, algorithmic excellence is defined by optimal performance in worst-case scenarios. Take for instance a situation where a player deliberately masks aspects of their gameplay to deceive the algorithm -this possibility must be factored into the decision tree analysis. Failing to account for such deceptive strategies not only leads to suboptimal play but also results in decreased algorithmic efficiency. This critical consideration of intentional deception was a missing element in previous generations of poker-playing algorithms.
For technical details and a more thorough explanation, the paper is available at: https://hal.science/hal-04416177.

3.  AI algorithms in game playing

AI aims to help playing agents to make intelligent decisions in order to maximize their objective. Here are some common algorithms:

1. Monte-Carlo Tree Search     (MCTS)

Monte Carlo TreeSearch (MCTS) represents a powerful fusion of systematic tree search and stochastic simulation, designed to navigate the decision-making complexity of advanced gaming scenarios. At its foundation lies the Upper Confidence Bound(UCB), a mathematical principle that masterfully balances the need to discover unexplored possibilities while refining proven strategies. The four key phases of MCTS are:

●     Selection: Navigate through established paths in the search tree.

●     Expansion: Add a new node to explore.

●     Simulation: Perform random simulations from the new node.

●     Back propagation: Update the tree with results from the simulation.

2. Genetic algorithms

Drawing inspiration from biological evolution, genetic algorithms and evolutionary computation harness nature's optimization principles to craft sophisticated gaming strategies. These systems mirror natural selection through a methodical process of digital evolution: they begin by creating a diverse population of potential solutions, rigorously assess their performance, and then systematically refine them using genetic operations like inheritance, mutation, and crossover. This biomimetic approach has proven particularly powerful in developing intricate gaming strategies, with notable success in simulation games. A prime example can be found in StarCraft, where evolutionary algorithms have enabled non-player characters (NPCs) to exhibit sophisticated and unpredictable behaviours, significantly enhancing both the game's difficulty curve and player engagement.

3. Neural Networks (NNs)

At the heart of contemporary game AI systems lie neural networks, sophisticated architectures that serve as the cognitive engine for artificial game players. These systems develop their decision-making capabilities by analyzing vast repositories of gaming data, learning to recognize intricate patterns and construct meaningful representations that guide strategic choices. Through extensive training on diverse game states and their corresponding actions, these networks develop the ability to both anticipate optimal moves and assess positional strength with remarkable precision.

The integration of deep neural networks (DNNs) -characterized by their multiple layers and extensive parametric complexity -with Monte Carlo Tree Search (MCTS)has yielded extraordinary results, particularly in games like Go and chess.This field continues to evolve rapidly, with recent innovations introducing transformer-based architectures and agent-centric frameworks to further advance game-playing capabilities.

4. Reinforcement Learning (RL)

RL trains AI agents to make decisions by rewarding desirable outcomes, which is essential for mastering games through trial and error. It uses Q-learning and policy gradients to teach agents the value of actions in each state and improve their reward outcomes.

Deep reinforcement learning, which combines RL and neural networks, has helped AI's beat top human players in complex games like StarCraft II.

‍