[Paper Review] A survey of air combat behavior modeling using machine learning

Patrick Ribu Gorton, Andreas Strand+1 more|arXiv (Cornell University)|Apr 22, 2024

Military Defense Systems Analysis4 citations

TL;DR

This survey investigates machine learning techniques—particularly reinforcement and imitation learning—for modeling realistic air combat behaviors in simulation, addressing limitations of traditional manual methods. It identifies key challenges in adaptability, multi-agent coordination, and deployment standardization, and proposes four strategic recommendations to advance the field toward scalable, realistic, and interoperable AI-driven air combat agents.

ABSTRACT

With the recent advances in machine learning, creating agents that behave realistically in simulated air combat has become a growing field of interest. This survey explores the application of machine learning techniques for modeling air combat behavior, motivated by the potential to enhance simulation-based pilot training. Current simulated entities tend to lack realistic behavior, and traditional behavior modeling is labor-intensive and prone to loss of essential domain knowledge between development steps. Advancements in reinforcement learning and imitation learning algorithms have demonstrated that agents may learn complex behavior from data, which could be faster and more scalable than manual methods. Yet, making adaptive agents capable of performing tactical maneuvers and operating weapons and sensors still poses a significant challenge. The survey examines applications, behavior model types, prevalent machine learning methods, and the technical and human challenges in developing adaptive and realistically behaving agents. Another challenge is the transfer of agents from learning environments to military simulation systems and the consequent demand for standardization. Four primary recommendations are presented regarding increased emphasis on beyond-visual-range scenarios, multi-agent machine learning and cooperation, utilization of hierarchical behavior models, and initiatives for standardization and research collaboration. These recommendations aim to address current issues and guide the development of more comprehensive, adaptable, and realistic machine learning-based behavior models for air combat applications.

Motivation & Objective

• To analyze current machine learning approaches for modeling realistic air combat behaviors in simulation environments.
• To identify limitations in existing behavior modeling, including high labor costs and loss of domain knowledge in traditional methods.
• To evaluate the potential of reinforcement and imitation learning for creating adaptive, scalable, and realistic AI agents.
• To address challenges in transferring learned agents from training environments to operational military simulation systems.
• To propose strategic recommendations for future research, including focus on beyond-visual-range combat, multi-agent cooperation, hierarchical models, and standardization.

Proposed method

• Systematic review of peer-reviewed literature on machine learning applications in air combat behavior modeling (2010–2024).
• Categorization of behavior model types, including rule-based, hybrid, and end-to-end learned models.
• Analysis of prevalent machine learning methods, with emphasis on deep reinforcement learning and imitation learning architectures.
• Evaluation of technical challenges such as reward shaping, generalization, and simulation-to-reality transfer.
• Examination of human factors, including pilot trust, interpretability, and training integration.
• Identification of gaps in multi-agent coordination, hierarchical decision-making, and standardization frameworks.

Experimental results

Research questions

• RQ1 How do modern machine learning techniques improve the realism and adaptability of AI-controlled air combat entities compared to traditional rule-based systems?
• RQ2 What are the key technical and human challenges in deploying learned agents in military simulation environments?
• RQ3 To what extent can imitation and reinforcement learning enable agents to master complex tactical maneuvers and sensor/weapon engagement?
• RQ4 How can hierarchical behavior modeling enhance the scalability and interpretability of AI-driven air combat agents?
• RQ5 What role can standardization and collaborative research play in advancing interoperability and real-world deployment of ML-based air combat models?

Key findings

• Reinforcement and imitation learning enable faster, more scalable development of complex air combat behaviors compared to manual rule-based approaches.
• Current simulated entities often lack realism due to oversimplified or static behavior models, limiting their effectiveness in pilot training.
• Transfer of agents from training environments to military simulation platforms remains a major challenge due to domain shift and lack of standardization.
• Multi-agent learning and cooperation are underexplored but critical for modeling realistic air combat scenarios involving team tactics.
• Hierarchical behavior models show promise in improving interpretability and modularity of learned policies.
• Standardization initiatives and collaborative research are essential to ensure interoperability, scalability, and long-term adoption of ML-based air combat systems.