Procedural Content Generation (PCG) is a trending technique in content authoring for Independent Game Developers (IGD), recent success in games such as No Man’s Sky has led developers to investigate this newly developed technology.
Now more than ever, Game AI has to adapt to a changing environment and delivering a unique gaming experience on Run-Time. A selection of Ambient AI (AAI) Techniques is selected for identifying the most suited technique for Procedural Games (PGS). In the selection process, a prototype of each AAI technique that illustrates its core concepts is simulated and tested in a semi – generated environment that resembles certain elements in a PE. Furthermore, each technique will be subject to testing sessions in order to evaluate the effectiveness of the approach of each technique based upon comparisons conducted between the simulations.
Table of Contents
1. ABSTRACT
2. RESEARCH STATEMENT
3. RESEARCH METHODOLOGY
4. RESEARCH SCOPE
5. RESEARCH LIMITATIONS
6. AI IN GAMES: INTRODUCTION
7. THE ROLE OF AMBIENT AI IN GAMES
8. AMBIENT AI TECHNIQUES: AN OVERVIEW
9. PE AND AI: AN OVERVIEW
10. AI AND PE: FURTHER CASE STUDIES
11. AAI TECHNIQUES: FURTHER ANALYSIS
12. AAI TECHNIQUES: SELECTION CRITERIA
13. SIMULATION SCENARIOS: DESIGN OVERVIEW
14. TEST CASES: OVERVIEW & DEFINITION
15. SIMULATION SCENARIOS: IMPLEMENTATION OVERVIEW
16. TEST CASES: ANALYSIS OF DATA GATHERED
17. RESEARCH CONCLUSION
18. FUTURE RESEARCH
Research Objectives and Core Themes
This thesis investigates and evaluates different Ambient AI (AAI) techniques to determine their suitability for creating immersive background ambience in procedurally generated game environments. The study aims to bridge the gap between procedural world generation and the need for believable, living background characters.
- Comparison of Ambient AI techniques: Artificial Life, Scheduling, and Smart Zones.
- Evaluation of performance impacts on game engines (Unity3D).
- Scalability assessment of AI agents within simulated environments.
- Analysis of run-time adaptability for procedural content generation.
- Development of test scenarios based on typical RPG ambient environments.
Excerpt from the Book
Smart Zones
According to Cloderic Mars in his Game AI Pro 2 article titled “Smart Zones to create the Ambience of Life” smart zones are a newly developed concept that aims to design creditable, consistent and interactive ambient life (Mike Lewis, 2015). Smart zones are composed of “Living Scenes”. A living scene is a set of NPCS interacting with and possibly with players. Furthermore, living scenes are designed to fit in the context of their virtual environment (Mike Lewis, 2015).
Each NPC participating in a scene fulfills a role, each role is defined by a set of behaviors that will be executed by the NPCS to achieve the scene. The behaviors assigned to each role are orchestrated in order to achieve the collective ambience required of the living scene. Furthermore, for executing a living scene, it is defined through a smart zone. Smart zones are a standalone module present in the virtual environment that is responsible for managing the lifetime of one living scene. Moreover, the aim of smart zones is to split the design of AAI into two individual layers; individual NPC behaviors and collective living scene behaviors (Cloderic Mars, 2015).
Summary of Chapters
AI IN GAMES: INTRODUCTION: Defines the role of AI in games as a tool for immersion and establishes the need for game AI to adapt to dynamic environments.
THE ROLE OF AMBIENT AI IN GAMES: Investigates the function of Ambient AI through case studies of commercial games to understand how background characters contribute to presence.
AMBIENT AI TECHNIQUES: AN OVERVIEW: Provides a theoretical foundation for various techniques including Artificial Life, Scheduling, CiF, Smart Zones, and Ambient Animation Loops.
PE AND AI: AN OVERVIEW: Discusses the challenges of integrating AI into procedural environments, specifically highlighting the issue of Context Correct Spawning.
AI AND PE: FURTHER CASE STUDIES: Derives potential criteria for effective AI in procedural settings, such as performance, scalability, and ease of implementation.
AAI TECHNIQUES: FURTHER ANALYSIS: Critically evaluates the previously mentioned techniques against the established hypothesis and research criteria.
AAI TECHNIQUES: SELECTION CRITERIA: Outlines the benchmarking methodology, focusing on performance quantification, scalability, and run-time adaptability.
SIMULATION SCENARIOS: DESIGN OVERVIEW: Defines hypothetical RPG-inspired scenarios for testing the chosen AAI techniques.
TEST CASES: OVERVIEW & DEFINITION: Establishes the baseline metrics and hardware specifications required to ensure a controlled testing environment.
SIMULATION SCENARIOS: IMPLEMENTATION OVERVIEW: Details the technical implementation and modifications made to the AAI prototypes within the Unity3D engine.
TEST CASES: ANALYSIS OF DATA GATHERED: Presents the quantitative findings from performance and scalability tests alongside qualitative arguments on adaptability.
RESEARCH CONCLUSION: Synthesizes the findings and provides an expert opinion on which AAI techniques are most viable for modern procedural game development.
FUTURE RESEARCH: Identifies potential areas for further study, specifically suggesting the use of fully-fledged procedural environments and long-term simulation testing.
Keywords
Ambient AI, Procedural Content Generation, Game AI, NPC, Unity3D, Simulation, Background Ambience, Performance Testing, Scalability, Run-time Adaptability, Smart Zones, Scheduling, Artificial Life, RPG, Game Engine
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on identifying and evaluating effective Ambient AI (AAI) techniques that can be applied to simulate realistic background ambience in procedurally generated environments.
What are the central themes discussed in this work?
The core themes include the role of background characters in user immersion, the technical challenges of AI in procedural worlds, and the comparative effectiveness of different AAI architectures like Smart Zones and Scheduling.
What is the main goal or research question?
The goal is to determine which AAI techniques are most suitable for simulating background ambience in procedural games, focusing on performance, scalability, and run-time adaptability.
Which scientific methods are utilized for this analysis?
The study utilizes both quantitative methods, such as performance profiling within the Unity3D engine, and qualitative methods, such as comparative analysis and theoretical evaluation of AI architectures.
What is the primary content covered in the main chapters?
The main chapters cover the theoretical overview of AAI, the design of test simulations based on RPG scenarios, the gathering of performance data, and a final critical analysis of the techniques tested.
Which keywords best characterize this research?
Key terms include Ambient AI, Procedural Content Generation, Game AI, NPC, Scalability, and Run-time Adaptability.
How does Context Correct Spawning influence the simulation?
Context Correct Spawning (CCS) is used to ensure that NPCs are spawned in positions and configurations that make sense for their assigned ambient behavior, which is essential for maintaining believability in procedural environments.
Why were the Artificial Life and CiF techniques excluded from the final simulation?
They were excluded due to a lack of qualitative evidence regarding their practical utility for background ambience in procedural RPG settings and their inherent conflict with the fixed requirements of the research hypothesis.
What is the main finding regarding Smart Zones?
While Smart Zones offer the most potential for high-quality, collective ambience and agent-level orchestration, they are the most computationally complex and show instability during scalability testing compared to simpler techniques.
- Quote paper
- Mohamed Serry (Author), 2016, Investigating Ambient AI Techniques Suited For Procedural Worlds, Munich, GRIN Verlag, https://www.grin.com/document/358129
