AI for the masses

Implementing Artificial Intelligence for Non-Player Characters in Video Games

Artificial Intelligence (AI) has become an integral part of modern video game development, enhancing the gaming experience by making non-player characters (NPCs) more realistic and interactive. NPCs, controlled by the game’s AI, can exhibit complex behaviors, make decisions, and adapt to the player’s actions, thereby creating a dynamic and immersive gaming environment. This essay will explore the process of implementing AI for NPCs in video games.

Understanding AI in Video Games

AI in video games is fundamentally different from traditional AI. While traditional AI aims to create a system that can perform tasks that would require human intelligence, AI in video games is designed to create an enjoyable and engaging experience for the player. This often involves creating NPCs that behave in a believable and predictable manner, rather than exhibiting true intelligence.

AI Techniques for NPCs

  1. Finite State Machines (FSM): FSM is a simple AI technique where an NPC can be in one of a finite number of states, such as patrolling, chasing, or attacking. The NPC transitions between these states based on certain conditions, such as the player’s proximity.
  2. Behavior Trees: A more advanced technique, behavior trees, allow for more complex NPC behavior by structuring AI as a tree of tasks. These tasks can be simple actions, like moving to a location, or more complex behaviors composed of other tasks.
  3. Utility AI: This technique involves assigning a utility score to different actions based on the current state of the game. The NPC then performs the action with the highest utility score. This allows for more dynamic and adaptable NPC behavior.
  4. Machine Learning: Some games use machine learning techniques to train NPCs. This involves using large amounts of data to train an NPC to respond to different situations. This can result in more unpredictable and realistic NPC behavior.

Implementing AI for NPCs

The first step in implementing AI for NPCs is to define the desired behavior. This could be as simple as an NPC that patrols a certain area, or as complex as an NPC that can engage in combat, navigate complex environments, and interact with the player.

Once the desired behavior is defined, the appropriate AI technique can be selected. For simple behaviors, a FSM may be sufficient. For more complex behaviors, a behavior tree or utility AI may be more appropriate. If the goal is to create an NPC that can learn and adapt, machine learning techniques may be used.

After selecting the AI technique, the next step is to implement it. This involves programming the NPC to perform the desired actions and react to the game environment. This can be a complex process, requiring a deep understanding of both programming and game design.

Testing and refining the AI is a crucial part of the process. This involves playing the game and observing the NPC’s behavior to ensure it behaves as expected. If the NPC’s behavior is not as desired, the AI may need to be adjusted or a different AI technique may need to be used.


Libraries for Implementing AI in Video Games

The implementation of AI in video games has been made significantly easier with the advent of various libraries and frameworks. These tools abstract away many of the complexities associated with AI, allowing developers to focus on creating engaging and dynamic NPCs. This chapter will explore some of the most popular libraries available for implementing AI in video games.

  1. TensorFlow and PyTorch

For developers interested in implementing machine learning-based AI, TensorFlow and PyTorch are two of the most popular libraries. Both libraries provide a comprehensive ecosystem of tools, libraries, and community resources that help researchers and developers build and deploy machine learning models. They support a wide range of neural network architectures and provide tools for training models, preparing data, and evaluating performance.

  1. Scikit-learn

Scikit-learn is a Python library that provides simple and efficient tools for predictive data analysis. It is built on NumPy, SciPy, and matplotlib, and it is open source and commercially usable. While not specifically designed for video games, it can be used to implement machine learning-based AI for NPCs.

  1. Unity ML-Agents

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. This can be used to create NPCs that can learn and adapt to the player’s actions.

  1. BehaviorTree.CPP

BehaviorTree.CPP is a C++ library for creating behavior trees. It is designed to be used in real-time applications like video games. It provides a way to create complex, hierarchical, and reusable behaviors for NPCs.

  1. Unreal Engine’s AI Tools

Unreal Engine, one of the most popular game development engines, provides a suite of AI tools. This includes a behavior tree implementation, a navigation system for pathfinding, and a perception system for sensing the game environment. These tools can be used to create complex and dynamic NPC behaviors.

  1. OpenAI Gym

OpenAI Gym is a Python library for developing and comparing reinforcement learning algorithms. It provides a wide variety of environments for training agents, including classic control tasks, Atari 2600 games, and simulated robotics tasks. While not specifically designed for video game development, it can be used to train machine learning-based AI for NPCs.

There are numerous libraries available for implementing AI in video games, each with its own strengths and weaknesses. The choice of library depends on the specific requirements of the game and the desired behavior of the NPCs. Regardless of the library chosen, the goal is the same: to create engaging and dynamic NPCs that enhance the player’s gaming experience. As AI technology continues to advance, we can expect to see even more powerful and easy-to-use libraries for game development in the future.

Implementing AI for NPCs in video games is a complex process that involves defining the desired behavior, selecting the appropriate AI technique, implementing the AI, and testing and refining the behavior. Despite the complexity, the use of AI in video games can greatly enhance the player’s experience by creating dynamic and interactive NPCs. As AI technology continues to advance, we can expect to see even more realistic and engaging NPCs in future video games.