AI in Board Games: How Neural Networks Beat Humans

Contents:

• Difficulties and achievements in the game of Go
• AlphaGo: Rethinking Artificial Intelligence in Go
• Prospects for artificial intelligence in board games

Difficulties and achievements in the game of Go

Since the advent of artificial intelligence capable of beating humans at board games such as checkers and chess, computers have demonstrated Impressive advances in information processing and strategic thinking have been made. However, the game of Go remained an unrivaled challenge for AI until recently. This is due to the high complexity and multifaceted nature of the game's strategy, where the number of possible positions exceeds even the number of atoms in the universe. However, with the development of machine learning algorithms and neural networks, AIs such as AlphaGo have overcome this barrier, demonstrating unique abilities in Go analysis and decision-making. This achievement marked a significant milestone in the history of artificial intelligence and opened new horizons for its application in various fields.

The Chinook program, created under the direction of Professor Jonathan Schaeffer, has held the world checkers championship since 1994. It became the first program in history to win this title, an achievement officially recorded in the Guinness Book of World Records in 1996. Chinook's success was a significant milestone in artificial intelligence and computer gaming, demonstrating the power of algorithms and computing technologies in solving complex strategic problems. Chess computers, beginning with IBM's Deep Blue's historic victory over grandmaster Garry Kasparov in 1997, have significantly changed the world of chess. Today, these powerful programs demonstrate a level of play unmatched by even the most experienced professional players. As a result, many grandmasters prefer to refrain from matches against computer programs, as the competition has become markedly unequal. Modern chess engines use complex algorithms and extensive databases, allowing them to analyze millions of positions in a short time. This leads to the fact that the level of play of computer systems continues to rise, leaving behind traditional approaches and strategies used by people.

The algorithms used to develop Go-playing programs between 1990 and 2010 were not adapted to the specifics of this complex game. Their primary methodology relied on brute-force attempts, which limited their effectiveness and prevented them from achieving significant results. To improve the programs' performance, it was necessary to account for Go's unique characteristics and develop more advanced strategies, which only became possible with the advent of new technologies and algorithms.

Go, a game widely popular in Asia, has long been humanity's last bastion in the fight against artificial intelligence. Until 2015, the best AI programs demonstrated only amateur-level performance, leading experts to believe that the creation of truly strong Go AI would not happen before 2025. However, despite these predictions, advances in machine learning and neural networks have significantly accelerated progress, leading to unexpected advances in the field.

The complexity of Go explains why traditional computers are unable to efficiently calculate all possible variations. To further understand this complexity, one can use a formula that estimates the size of the game tree: bd, where b denotes the number of possible moves in each position and d is the average game length. This formula highlights the large number of combinations that must be considered for an optimal strategy. This is precisely why Go is considered one of the most challenging board games, requiring a high level of intellectual effort and deep analysis. Developing algorithms that can handle such complexities has become an important challenge in artificial intelligence.

The complexity parameters of board games can be illustrated with a comparison table. For example, for tic-tac-toe, the total number of possible combinations is 49 = 262,144, while for checkers this number is significantly higher and reaches 2,870 = 20,001,482,841,082,030,545,089,749,452,286. Such data highlights the difference in complexity and strategic thinking required for each of the games.

The development of the Chinook program took 18 years of continuous computing, during which up to 200 computers worked simultaneously. In 2007, Schaeffer officially completed the project, declaring that his program completely covered all possible combinations in checkers, making it unbeatable.

An opponent can only hope for a draw, which is an extremely difficult task, since even a minor mistake can lead to defeat.

Creating a complete game tree in chess remains impossible today due to the huge number of possible positions. Modern computers, such as Deep Blue, use optimized algorithms that analyze the most likely combinations of moves several moves ahead. This strategy has proven effective and ensured victory over chess masters.

This approach does not make programs competitive in Go due to the colossal complexity of this gameplay. The size of the tree of possible moves in Go is 80 orders of magnitude larger than that of chess, which is several times greater than the number of atoms in the universe. Predicting such a large number of possible moves is beyond the capabilities of any modern supercomputer.

AlphaGo: Rethinking Artificial Intelligence in Go

Created by the British startup DeepMind in 2015, AlphaGo has revolutionized artificial intelligence and the game of Go. This neural network program builds on the achievements of TD-Gammon, developed 20 years ago, which laid the foundation for new ideas and technologies. AlphaGo uses deep learning and machine learning methods to analyze a huge number of possible moves, allowing it to effectively compete with the best Go players. The innovative approaches used in AlphaGo have not only changed the understanding of AI's capabilities but also opened up new horizons for its application in various fields.

Using modern methods such as deep learning, AlphaGo employs two complex neural networks to analyze game actions. A policy network predicts the most optimal moves, while a value network calculates the probability of winning in each specific position. This significantly simplifies the decision-making process and allows AlphaGo to achieve high results in Go.

AlphaGo differs from Deep Blue's classic approach, which involved trying millions of game positions. Instead, AlphaGo uses "digital intuition," predicting developments and analyzing possible game outcomes. This approach makes its strategy more similar to human thinking, allowing it to make decisions based on an intuitive analysis of the board situation. This opens new horizons in the field of artificial intelligence and in the understanding of games like Go.

AlphaGo achieved its first major victory in a match against European Champion Fan Hui, winning 5-0. However, the real sensation occurred in 2016, when AlphaGo faced Go legend Lee Sedol. This match became historic, ending with a score of 4:1 in favor of AlphaGo, which demonstrated significant progress in the field of artificial intelligence and its ability to think strategically.

Go players were impressed by AlphaGo's prowess. As Andy Jackson, vice president of the American Go Association, noted, the program demonstrated original strategies that sometimes surprised even seasoned masters. Elements of the game previously considered mistakes actually led to victories, challenging established notions about Go and its traditions. AlphaGo opened new horizons in understanding the game, emphasizing that artificial intelligence is capable of not only analyzing but also creatively solving complex Go problems.

Lee Sedol, the only person to have won a single game against AlphaGo, admitted that his perception of the game had changed dramatically. He realized that he could learn a lot from this artificial intelligence program. "I realized that I need to improve my skills," " he noted. This recognition underscores the importance of continuous learning and adaptation in the rapidly evolving world of gaming. Fan Hui noted that AlphaGo allowed him to significantly improve his skills, which contributed to his 300-place ranking increase. Cho Hye-young, a leading female gamer, also expressed a desire to study AlphaGo's strategies, emphasizing the program's unique approach. This situation demonstrates how artificial intelligence influences the development of players' skills and increases their level of mastery in the game.

Cho Hye-young expressed her admiration for AlphaGo, stating: "I want to learn from her and Learn from its game. AlphaGo seems to know everything. This artificial intelligence has amazed the world with its ability to learn and think strategically, making it a remarkable example in the field of technology and the game of Go.

Despite Lee Sedol winning one game, independent experts unanimously agreed that AlphaGo demonstrated clear superiority over the human player. This event was a landmark, marking a new era in understanding the potential of artificial intelligence in strategy games. AlphaGo not only demonstrated high analytical abilities but also opened new horizons for the application of AI in various fields, including learning, data analysis, and complex problem solving.

Prospects for Artificial Intelligence in Board Games

Modern neural networks based on reinforcement learning algorithms have demonstrated impressive results in board games, surpassing human achievements. This event marked the transition from traditional methods based on brute force to more advanced technologies that are changing the approach to strategy games, including chess. The use of neural networks not only improves the level of play but also opens new horizons for analysis and strategy, making them an important tool in modern gaming.

Artificial intelligence (AI) today demonstrates impressive abilities in learning and developing unique strategies for evaluating game situations. Through training on millions of played games, such systems develop intuition that often surpasses the analytical skills of the world's best players. This makes AI a valuable tool in the field of gaming, opening new horizons for analysis and strategy. AI-powered technologies are becoming an integral part of the gaming industry, improving both the learning process and the gaming experience.

Professional players can gain significant advantages by observing AI in action. This creates new learning opportunities, where machines not only learn from humans, but players can also learn from AI experiences and strategies. Studying the algorithms and approaches used by AI allows for skill improvement and the development of tactical thinking. Interaction with AI is becoming an important tool for improving one's gameplay and adapting to different playstyles.

Interaction with AI is a key aspect of the development of gaming communities and technologies. Research shows that the use of AI can significantly improve user gaming skills and stimulate the creation of new and innovative strategies in games. This approach not only enriches the gaming experience but also contributes to the evolution of the gaming industry itself, opening new horizons for developers and players.

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