🤖 AI Summary
This study addresses the reconstruction of ancient human movement, interaction, and transport behaviors in complex terrains from static archaeological evidence. To this end, we propose a multi-agent simulation framework that integrates high-fidelity 3D terrain modeling, heterogeneous agents—including humans, pack animals, and wheeled vehicles—and a hybrid navigation mechanism combining global path planning with reinforcement learning–based local dynamic adjustments. The framework incorporates empirically derived mobility parameters such as load capacity and slope tolerance, enabling efficient and interpretable large-scale simulations. We demonstrate its utility through applications in pursuit-evasion scenarios and comparative analyses of transport modes, revealing the significant influence of terrain morphology, visibility, and agent heterogeneity on ancient mobility patterns.
📝 Abstract
Understanding mobility, movement, and interaction in archaeological landscapes is essential for interpreting past human behavior, transport strategies, and spatial organization, yet such processes are difficult to reconstruct from static archaeological evidence alone. This paper presents a multi-agent-based modeling framework for simulating archaeological mobility in uneven landscapes, integrating realistic terrain reconstruction, heterogeneous agent modeling, and adaptive navigation strategies. The proposed approach combines global path planning with local dynamic adaptation, through reinforcment learning, enabling agents to respond efficiently to dynamic obstacles and interactions without costly global replanning. Real-world digital elevation data are processed into high-fidelity three-dimensional environments, preserving slope and terrain constraints that directly influence agent movement. The framework explicitly models diverse agent types, including human groups and animal-based transport systems, each parameterized by empirically grounded mobility characteristics such as load, slope tolerance, and physical dimensions. Two archaeological-inspired use cases demonstrate the applicability of the approach: a terrain-aware pursuit and evasion scenario and a comparative transport analysis involving pack animals and wheeled carts. The results highlight the impact of terrain morphology, visibility, and agent heterogeneity on movement outcomes, while the proposed hybrid navigation strategy provides a computationally efficient and interpretable solution for large-scale, dynamic archaeological simulations.