## Algorithms for Game AI  
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Footnote 1: [[https://www.cai.org/](https://www.cai.org/)]([https://www.cai.org/](https://www.cai.org/))  
Games have long been benchmarks for AI algorithms and, with the boost of computational power and the application of new algorithms, AI systems have achieved superhuman performance in games for which it was once thought that they could only be mastered by humans due to their high complexity. Since Deep Blue beat professional human players in chess [1], many milestones have been reported in various games, from board games like Go [2, 3, 4] and card games like Texas Hold'em [5, 6, 7] to video games like StarCraft [8], Dota 2 [9], and HoK [10]. There are two reasons why games are excellent benchmarks for AI algorithms. First, games are born to test and challenge human intelligence--just as children learn about the world by playing with games and toys during their first years of life, the diversity of games can provide a rich context to test different cognitive and decision-making capabilities. For example, board games, with their formalized state representation and perfect information, require searching and planning from the current game state. Card games, with their non-deterministic transition and imperfect information, reveal more sophisticated strategies, such as bluffing and deception, skills that are normally reserved for humans. Video games, with their high-dimensional states and long episodes, require feature extraction, memorization, long-term planning, and multi-agent cooperation and competition. These characteristics make games strong testbeds for the gradual skill progression of AI. Second, solving complex real-world problems usually requires repeated trial and error, which can be very costly. By simulating or emulating real-world scenarios, games provide a low-cost or even zero-cost platform to validate various algorithms and solutions. In a broad sense, games are not limited to those that people play for entertainment, but any decision-making problems can be modeled and simulated as games, from simple robot control to complex scenarios of resource allocation, scheduling, and routing across various industrial fields. Algorithms proposed and developed to play games are eventually applied to various real-world problems for social benefit in all aspects of life. This Special Issue entitled \"Algorithms for Game AI\" covers new and innovative approaches for solving game AI problems. These approaches range from traditional algorithms like planning and searching, to modern algorithms such as deep reinforcement learning. The papers in this Special Issue address both the theoretical and practical challenges of the application of these algorithms. AI techniques have been implemented in gaming industry with a wide range of applications [11], such as procedure content generation (PCG), where algorithms autonomously create game worlds, levels, and assets to enhance replayability, and AI-driven non-player character (NPC) behavior, simulating human-like decision-making and emotional responses for better engagement. Nevertheless, we mainly focused on the application of building game-playing agents, because of the inherent challenges and the potential for solvingreal-world decision-making problems. In this field, game AI algorithms can be divided into several categories, such as the following:  
* Search algorithms are used for real-time planning. These algorithms are designed to expand a search tree from the game state currently encountered to evaluate future states under different sequences of actions. Some algorithms like A* [12] leverage heuristic functions to guide the selection of unexplored nodes, which evaluate game states based on the domain knowledge of human players. In general, searching more deeply usually yields better policies because it eliminates some errors of value estimation by looking more steps ahead.