Mastering Enemy AI Development: Unleash the Secrets of Creating Realistic and Challenging Foes

Table of Contents

1. Introduction
2. Challenges in Enemy AI Development
3. The Fundamental Actions of Enemy AI
   • Action 1: Walking or Strafing
   • Action 2: Pathfinding to a Specific Target
   • Action 3: Melee Attacks
   • Action 4: Ranged Attacks
   • Action 5: Taking Cover
   • Action 6: Following Cryptic Movement
   • Additional Actions
4. Approaches to Enemy AI Development
   • Finite State Machines
   • Modular Systems and Templates
   • Creating Unique AI Behaviors
5. Sensor, Processor, and Actuator Model
   • The Role of Sensors in AI
   • Processing Logic and Decision Making
   • Actuators for Enemy AI Behavior
6. Creating a Modular AI Template
   • Player Sensor
   • Random Direction Logic
   • Pathfinding Logic
   • Obstacle Avoidance in Dreams
   • Basic Shooting Logic
   • Stealth System
   • Guard Pattern Behavior
   • Other Actions and Behaviors
7. Tips and Insights from Other Dreamers
8. Proof of Concept and Release
9. Conclusion

🤖 Advanced Enemy AI Development in Video Games

Have you ever wondered how enemy AI in video games works? Creating realistic and challenging AI is a complex task that requires careful planning and implementation. In this article, we will explore the challenges faced by game developers in enemy AI development and discuss the fundamental actions that make up enemy behavior. We will also delve into different approaches to AI development, including finite state machines and modular systems. Additionally, we will introduce a modular AI template created in Dreams, a popular PlayStation creation engine, and explain how it can be used to create unique enemy AI in games. So, let's dive into the fascinating world of advanced enemy AI development!

Introduction

Developing enemy AI for video games is no easy task. It requires a deep understanding of game development, programming, and system integration. However, the true challenge lies in creating AI behaviors that are not only realistic but also unpredictable. Two major challenges faced by game developers in enemy AI development are enemy positioning and pathfinding, and decision making.

Challenges in Enemy AI Development

Enemy positioning and pathfinding can be incredibly difficult to implement effectively. Getting enemies to navigate complex 3D environments, overcome obstacles, and find their way through the level is a daunting task. Additionally, decision making is a crucial aspect of enemy AI. When should enemies move, attack, take cover, or even retreat? These are questions that developers often lose sleep over.

One particular type of AI that poses an immense challenge is the kind found in games like Uncharted 4. These games feature vertical level designs and realistic human enemies that must pressure the player, take cover, flank, throw grenades, and realistically lose sight of the player only to try and find them again.

It's no wonder that many starting Game developers opt to create zombie survival games. Zombies, with their simple pathfinding and straightforward attack Patterns, are the easiest to implement as AI enemies. In fact, even my first shooter game featured zombie-like enemies.

However, when it comes to enemies in arena shooters and games like Dark Souls, the AI requirements become more demanding. But here's the thing – AI doesn't have to be overwhelming. By breaking down the behaviors into fundamental actions, we can create a satisfying combat experience with just a few actions.

The Fundamental Actions of Enemy AI

Let's take a closer look at the fundamental actions that can make up enemy behavior in a game:

🚶 Action 1: Walking or Strafing

In many games without cover systems, enemies will walk or strafe around in random directions. This is often seen in groups of enemies, where you don't want them to awkwardly stand in one place.

🗺️ Action 2: Pathfinding to a Specific Target

Enemies often have a specific target in mind, whether it's the player or a predetermined point in the level. Pathfinding involves finding a way to reach that target by avoiding obstacles along the way.

👊 Action 3: Melee Attacks

Almost every enemy in every game has some sort of melee or close-range attack. While the nuances of the attacks may vary, from a programming perspective, it is essentially the enemy moving towards the player and attempting to hit them.

🔫 Action 4: Ranged Attacks

Shooter enemies, as well as some melee combat games, often have enemies that can launch projectiles or use ranged attacks. These attacks require enemies to aim and fire at the player from a distance.

🛡️ Action 5: Taking Cover

In many games, enemies take cover to protect themselves from the player's attacks. A basic cover system allows enemies to crouch or hide behind objects for protection.

🚶‍♀️ Action 6: Following Cryptic Movement (Guard Patterns)

In stealth games or situations where enemies need to patrol an area, they may exhibit specific movement patterns. These patterns can involve following set paths or reacting to specific triggers, such as the player entering a certain area.

These fundamental actions form the building blocks of enemy behavior in games. While there are certainly more actions and actions specific to Triple A games, it's important to recognize that a satisfying combat loop can be created with just a few core actions.

Approaches to Enemy AI Development

In the world of game development, there are different approaches to creating enemy AI. Two common methods include finite state machines and modular systems.

Finite State Machines

Finite state machines (FSMs) are a common approach to AI development. In an FSM, an AI is defined by a set of states, where each state consists of multiple actions. Events or triggers in the game environment activate these states. For example, if an enemy is shot, it may enter a "take cover" state to find a safe spot.

While FSMs can be effective for creating AI behaviors, they can quickly become complex and difficult to manage. Interconnecting states without proper separation can lead to messy and unmanageable code, as I learned from my previous AI development experience.

Modular Systems and Templates

To overcome the complexity of FSMs, a more modular approach can be taken. By separating the different elements of AI (sensors, processors, and actuators), we can create a more manageable and customizable system. Sensors detect events or conditions in the game, processors interpret that information to make decisions, and actuators execute the desired actions.

By focusing on creating the necessary actions and sensors first, developers can provide a foundation for unique AI behaviors. This approach allows for flexibility and empowers creators to customize AI according to their specific needs.

Sensor, Processor, and Actuator Model

The sensor, processor, and actuator model is a framework that helps organize and conceptualize enemy AI development. Each element plays a distinct role in shaping the behavior of AI.

The Role of Sensors in AI

Sensors in AI detect and Gather information about the game world. They are responsible for identifying Relevant events or conditions that trigger specific behaviors. In the context of enemy AI, sensors can include player visibility, range, location, sound detection, or any other input that affects enemy behavior.

Processing Logic and Decision Making

Processors take the information collected by sensors and process it through logic to make decisions. Processors determine which actions the AI should take based on the input received. They are essentially the decision-making component of AI development.

Actuators for Enemy AI Behavior

Actuators are the components that execute the actions determined by the processor. They are responsible for the physical or virtual manifestation of AI behavior. Actuators can include movement controls, attack animations, covering animations, or any action that an enemy can perform.

Creating a Modular AI Template

With these concepts in mind, I set out to create a modular AI template using Dreams, the PlayStation creation engine. This template would allow creators to quickly and easily develop their own unique AI behaviors.

Player Sensor

The template starts with a player sensor that checks if the player is visible to the AI and within its field of view. This sensor provides valuable information for decision-making and allows for responsive enemy behavior.

Random Direction Logic

To create variation in enemy movement, I developed a logic chip that makes the AI walk in random directions at random speeds. This logic chip was actually created in a previous project and can be reused here.

Pathfinding Logic

Next, I incorporated a pathfinding logic that allows enemies to navigate to a specific target, such as the player or a random point in the level. Pathfinding involves obstacle avoidance, which can be challenging in Dreams due to the lack of a built-in Nav mesh system. However, by calculating 2D obstacle avoidance and using invisible walls, enemies can navigate the environment effectively.

Obstacle Avoidance in Dreams

In Dreams, obstacle avoidance can be achieved by calculating a 2D line through obstacles. While it's not as straightforward as using a Nav mesh, it is a viable solution. By creating invisible walls and using basic pathfinding logic, enemies can be prevented from walking off cliffs or areas they shouldn't traverse.

Basic Shooting Logic

No enemy AI would be complete without the ability to shoot at the player. I implemented both hitscan and projectile shooting logic to provide flexibility in enemy behavior. Hitscan shooting involves instantaneously detecting if the player is in the line of sight, while projectiles allow for more dynamic engagement.

Stealth System

Stealth is an essential aspect of many action games. To create a stealth system, I designed a view cone for the enemy. If the player remains in the view cone and becomes visible for a certain duration, the enemy's stealth is broken. The closer the player is to the enemy, the shorter the duration required to break stealth.

Guard Pattern Behavior

In games that require enemies to follow specific guard patterns or patrol routes, a guard pattern behavior is needed. I devised a simple yet effective solution in Dreams using a cube that can be entered as a playable character. The enemy follows the guard path defined by the creator, allowing for dynamic and customizable AI movement.

Other Actions and Behaviors

To further enhance enemy AI, I included additional actions such as melee attacks, jump attacks, burst firing, and a basic cover system. These actions provide variety and depth to combat encounters, creating a more engaging gameplay experience.

Tips and Insights from Other Dreamers

During my AI development journey, I reached out to other Dreamers for their insights and tips on enemy AI. One particularly helpful discovery was a pathfinding system created by Dreams logic user Vizium. Their system utilized four supplier zones, offering a simple yet effective pathfinding solution.

Proof of Concept and Release

After countless hours of development and relentless polishing, I was ready to release the modular AI template to the Dreams community. To ensure that the template was functional and effective, I implemented it in a canceled Dragon's Dogma-inspired game. The results were promising, and I finally felt a sense of accomplishment and relief.

Conclusion

Enemy AI development is a challenging yet rewarding aspect of game development. By understanding the fundamental actions, adopting modular approaches, and utilizing tools like the Dreams creation engine, developers can create complex and unique AI behaviors. The modular AI template showcased in this article is just one example of how groundbreaking advancements can be made in AI development. So, to all aspiring game developers and AI enthusiasts, embrace the possibilities and create your own AI masterpieces!

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