Enhance AI Behavior with Prediction Sense

Table of Contents

1. Introduction

2. Understanding the Problem

3. Adding the Prediction Sense

4. Configuring the Prediction Event

5. Setting the Requester and Predicted Actor

6. Setting the Prediction Time

7. Handling Predictions

8. Debugging and Fine-tuning

9. Fixing the Issue of Tracking Old Locations

10. Exploring the EQS System

11. Generating Items in the EQS System

12. Conclusion

Introduction

In this article, we will Delve into the world of AI Perception systems and focus on the prediction Sense. We will explore how prediction works and how it can be utilized to enhance the AI's ability to anticipate the character's movements. By understanding and implementing prediction sense, we can Create more realistic and dynamic AI behavior in our games.

Understanding the Problem

Before we dive into prediction sense, let's first understand the problem it aims to solve. When a character loses sight of its target, it typically tends to head towards the last known location. However, this approach fails to consider the target's actual movement direction. For example, if the character went around a pole, the AI should predict that the target has moved to the other side. To achieve this predictive behavior, we need to implement the prediction sense.

Adding the Prediction Sense

To begin, we need to add the prediction sense to our AI perception system. By accessing the AIS controller and going into the AI perception, we can add another perception sense configuration. We will set the handle sight sense to false and instead configure it as a prediction sense. The prediction sense will utilize the pawn prediction event to anticipate the target's future movements. We will provide the predicted actor and the requester (controlled pawn) to ensure accurate predictions.

Configuring the Prediction Event

In the prediction sense configuration, we specify the prediction time. This determines how far into the future the AI should predict the target's location. The prediction is calculated Based on the target's velocity, speed, and direction over time. It is essential to keep the prediction time relatively short to maintain accuracy. A prediction time of one to two seconds is generally ideal for most scenarios.

Setting the Requester and Predicted Actor

To configure the prediction sense accurately, we need to set the requester and predicted actor. The requester refers to the currently controlled pawn, typically the AI character itself. The predicted actor, on the other HAND, is the target the AI is continuously searching for, such as the player character. By checking against the clear character, we can specify the predicted actor accurately.

Handling Predictions

Once the prediction sense is set up, we need to handle the predictions in our event graph. By creating a new handle prediction event, we can process and react to the predictions made by the AI. We can plug this event into the stimulus and handle exceptions accordingly. This ensures that the AI tracks the target's predicted movements and adjusts its behavior accordingly.

Debugging and Fine-tuning

To Visualize and fine-tune the prediction sense, we can enable perception debugging. By turning on the perception debug mode, we can observe how the AI tracks the target's movements and whether the prediction time is set accurately. It is crucial to strike a balance with the prediction time to avoid either overly delayed or premature predictive behavior. By iteratively adjusting the prediction time, we can achieve the desired level of accuracy.

Fixing the Issue of Tracking Old Locations

One problem we might encounter is the AI continuing to head towards the old last known location even when a new prediction is available. To address this, we can modify the behavior tree and ensure that the move to last known location is based on the observed last known location rather than using the outdated information. By adjusting the tolerance and observing the blackboard value, we can make the AI move towards the new last known location accurately.

Exploring the EQS System

In the next part of our AI series, we will shift our focus to the EQS system (Environment Query System). The EQS system is a comprehensive system that allows for complex decision-making based on environmental queries. We will explore the basics of the EQS system and learn how to generate different items within it. The EQS system adds a layer of intelligence to AI behavior, enabling them to make informed decisions based on various factors in their environment.

Generating Items in the EQS System

Understanding how to generate different items within the EQS system is crucial for creating dynamic and reactive AI behavior. By leveraging the EQS system's capabilities, we can make our AI characters Interact with their surroundings intelligently. We will cover the fundamental concepts and provide practical examples to help You get started with generating items in the EQS system.

Conclusion

In conclusion, the prediction sense is a powerful tool in AI perception systems that allows the AI to anticipate the target's movements accurately. By implementing prediction sense, we can create more realistic and dynamic AI behavior in our games. Additionally, the EQS system further enhances AI decision-making and responsiveness by generating items based on environmental queries. By combining the prediction sense and the EQS system, we can create truly immersive and engaging AI experiences.

Highlights

• Understand the problem faced when AI loses sight of the target
• Implement the prediction sense to anticipate target movements
• Configure the prediction event and set the requester and predicted actor
• Handle predictions and fine-tune the prediction time
• Fix the issue of tracking old locations
• Explore the EQS system for advanced decision-making
• Learn to generate different items in the EQS system
• Create realistic and dynamic AI behavior in games
• Enhance AI decision-making and responsiveness
• Achieve immersive and engaging AI experiences

FAQ

Q: Why is prediction sense important in AI perception systems? A: Prediction sense allows the AI to anticipate the target's movements accurately, resulting in more realistic and dynamic behavior.

Q: How do you fine-tune the prediction time for optimal accuracy? A: It is essential to strike a balance with the prediction time. Iteratively adjusting the time and observing the AI's behavior can help achieve the desired level of accuracy.

Q: What is the EQS system, and how does it enhance AI decision-making? A: The EQS system (Environment Query System) allows for complex decision-making based on environmental queries. It adds a layer of intelligence to AI behavior by enabling them to make informed decisions based on various factors in their environment.

Q: How can the EQS system be used to generate items in AI behavior? A: The EQS system can be utilized to generate different items within the environment based on specific queries. This allows AI characters to interact with their surroundings intelligently and enhance overall gameplay immersion.