How can game developers create AI teams that work together?

1 Why AI teams matter

AI teams can enhance the gameplay experience by providing more realistic, dynamic, and engaging opponents or allies for the player. AI teams can also create emergent and diverse scenarios that increase the replay value and the challenge of the game. Moreover, AI teams can enable new forms of social interaction and collaboration among players, such as co-op modes, team-based competitions, or shared narratives.

2 How to design AI teams

The first step to create AI teams that work together is to define their goals, roles, and behaviors. You need to decide what the AI team wants to achieve, how each member contributes to that goal, and how they interact with each other and the environment. You also need to consider the level of autonomy, coordination, and communication that the AI team requires, and how to balance them with the player's agency and expectations.

3 How to implement AI teams

The second step to create AI teams that work together is to choose the appropriate algorithms and frameworks that can support their logic and decision-making. There are many options available, such as finite state machines, behavior trees, utility systems, planning systems, neural networks, or reinforcement learning. Each option has its advantages and disadvantages, depending on the complexity, flexibility, and performance of the AI team. You also need to test and debug your AI team to ensure that it behaves as intended and responds to different situations.

4 How to evaluate AI teams

The third step to create AI teams that work together is to measure their effectiveness and quality. You need to define the criteria and metrics that can evaluate how well the AI team achieves its goals, how it adapts to changing conditions, how it cooperates and communicates with other agents, and how it influences the player's experience and satisfaction. You also need to collect and analyze data from your AI team, such as logs, traces, or feedback, to identify and improve its strengths and weaknesses.

5 How to learn from AI teams

The fourth step to create AI teams that work together is to leverage their potential to learn from each other and from the player. You can use techniques such as imitation learning, transfer learning, or self-play to enable your AI team to mimic, generalize, or optimize its behavior based on the data and feedback from other agents or the player. You can also use techniques such as procedural content generation, dynamic difficulty adjustment, or adaptive storytelling to create more varied and personalized content and narratives for your AI team and the player.

6 How to inspire AI teams

The fifth step to create AI teams that work together is to infuse them with personality, emotion, and creativity. You can use techniques such as natural language processing, facial animation, or generative art to give your AI team more expressive and immersive ways to communicate and interact with the player and other agents. You can also use techniques such as intrinsic motivation, curiosity, or serendipity to give your AI team more autonomy and agency to explore and discover new possibilities and outcomes in the game world.