Using Behavior Objects to Manage Complexity in Virtual Worlds

TL;DR

This paper introduces behavior objects as a novel, object-oriented approach to managing NPC AI complexity in large open-world games, aiming to improve development efficiency and AI sophistication.

Contribution

It proposes behavior objects inspired by object-oriented programming to encapsulate NPC behaviors, embedding intelligence in the environment and managing complexity more effectively.

Findings

Behavior objects encapsulate multiple related behaviors.

Implementation details in behavior trees are discussed.

Lessons learned from developing an AAA open-world game.

Abstract

The quality of high-level AI of non-player characters (NPCs) in commercial open-world games (OWGs) has been increasing during the past years. However, due to constraints specific to the game industry, this increase has been slow and it has been driven by larger budgets rather than adoption of new complex AI techniques. Most of the contemporary AI is still expressed as hard-coded scripts. The complexity and manageability of the script codebase is one of the key limiting factors for further AI improvements. In this paper we address this issue. We present behavior objects - a general approach to development of NPC behaviors for large OWGs. Behavior objects are inspired by object-oriented programming and extend the concept of smart objects. Our approach promotes encapsulation of data and code for multiple related behaviors in one place, hiding internal details and embedding intelligence in the environment. Behavior objects are a natural abstraction of five different techniques that we have implemented to manage AI complexity in an upcoming AAA OWG. We report the details of the implementations in the context of behavior trees and the lessons learned during development. Our work should serve as inspiration for AI architecture designers from both the academia and the industry.