Creating efficient and flexible artificial intelligence (AI) systems for enemies in games without using complex and time-consuming solutions known as “crutches” requires structured approaches to behavior design. One such approach is state machines.

State Machines and their applications

State Machines are models of behavior where an object can be in one of a number of predefined states, and transitions between these states occur in response to certain events or conditions. In the context of AI enemies, this means that each enemy can be in the state of patrolling, pursuing, attacking, or resting, and transition between these states depending on the situation.

However, when expanding enemy behavior and adding new states, using simple state machines can lead to increased code complexity and crutches. This is due to the need to account for all possible combinations of states and transitions, making the system difficult to maintain and evolve.

Alternative approaches: Behavioral Trees and Utility AI

To overcome the limitations of traditional state machines, developers are increasingly turning to alternative methods such as behavioral trees and Utility AI.

Behavior Trees: This approach organizes AI behavior as a hierarchical structure where each node represents a task or condition. Behavioral trees allow for more flexible control of enemy behavior, making it easy to add new functionality and simplify debugging.

Utility AI: In this model, the AI evaluates different possible actions based on their utility in the current situation. Each action has a different value and the AI selects the most appropriate action depending on the context, enabling dynamic and adaptive behavior of enemies.

Recommendations for designing AI without crutches

Abstraction and modularity: Structure the AI code so that different aspects of behavior are separated into independent modules. This makes it easier to make changes and add new functionality without affecting existing code.

Leverage existing solutions: Whenever possible, use proven libraries and frameworks to implement AI, which avoids inventing your own solutions and allows you to focus on specific aspects of the game.

Testing and profiling: Regularly test and profile AI to identify bottlenecks and optimize performance without adding complexity to the architecture.

Using these approaches, developers can create complex and adaptive enemy behavior while avoiding creating convoluted and time-consuming systems that are difficult to maintain and expand.