What are the best ways to balance AI complexity and game performance?

1 Use Hierarchical Finite State Machines

A finite state machine (FSM) is a model of AI behavior that consists of a set of states and transitions between them. For example, an enemy AI might have states like idle, patrol, chase, attack, and flee. A hierarchical FSM is a nested structure of FSMs that allows you to create more modular and scalable AI systems. For example, you can have a top-level FSM that controls the general mood of the AI, such as aggressive, cautious, or friendly, and a lower-level FSM that controls the specific actions within each mood. This way, you can reuse and adapt your AI logic for different scenarios and characters, and reduce the number of transitions and conditions to check.

2 Use Behavior Trees

A behavior tree is another model of AI behavior that consists of a tree-like structure of nodes that represent tasks, decisions, and sequences. For example, a node might represent a task like move to a location, a decision like check if the player is in sight, or a sequence like patrol a route. A behavior tree allows you to create more flexible and dynamic AI systems that can handle complex and unpredictable situations. For example, you can use priority selectors, random selectors, parallel nodes, and decorators to modify the behavior of your AI based on various factors and events. Behavior trees are also easier to debug and maintain than FSMs, as they provide a clear visual representation of your AI logic.

3 Use Utility Theory

Utility theory is a model of AI decision making that assigns a numerical value to each possible action based on various criteria, such as goals, preferences, costs, and risks. For example, an AI might assign a utility value to each weapon based on its damage, range, accuracy, and ammo. The AI then chooses the action with the highest utility value, or a random action weighted by the utility values. Utility theory allows you to create more realistic and diverse AI systems that can adapt to changing circumstances and preferences. For example, you can use utility curves, formulas, or tables to define how the utility values change depending on the inputs and outputs.

5 Use Profiling Tools

Profiling tools are software tools that help you measure and analyze the performance of your game and its components, such as CPU, GPU, memory, and network. Profiling tools can help you identify and fix the bottlenecks and inefficiencies in your AI systems, such as memory leaks, CPU spikes, or network latency. For example, you can use profiling tools to monitor the CPU usage, memory allocation, and frame rate of your AI logic, and compare them with the game's overall performance. Profiling tools can also help you test and optimize your AI systems for different platforms, devices, and settings.