What does the term 'self-play' refer to in the context of AI systems like AlphaZero?

The term 'self-play' in the context of AI systems, particularly in applications like AlphaZero, refers to the practice of the AI learning from the outcomes of its own previous games. Through self-play, the AI engages in games against itself, allowing it to explore various strategies and tactics without requiring an external opponent. This method enables the AI to iteratively improve its performance, as it analyzes each game's results and adjusts its strategies accordingly.

In the case of AlphaZero, this approach is crucial because it allows the system to reach a high level of proficiency in complex games by creating a vast number of training scenarios. Each game played contributes to the AI's understanding of optimal moves and strategies, ultimately leading to enhanced decision-making capabilities. This learning mechanism is fundamentally different from relying solely on human opponents or pre-existing datasets, as it provides a more dynamic and self-contained learning environment.