Multi-Agent-Based Simulation of Archaeological Mobility in Uneven Landscapes

TL;DR

This paper introduces a multi-agent simulation framework for modeling archaeological mobility in uneven terrains, integrating realistic terrain data, diverse agent types, and adaptive navigation to better understand past human movement and transport strategies.

Contribution

It presents a novel hybrid navigation approach combining global path planning with local adaptation using reinforcement learning, tailored for archaeological landscape simulations.

Findings

Terrain morphology significantly influences agent movement outcomes.

Heterogeneous agents exhibit different mobility patterns based on their characteristics.

The hybrid navigation strategy improves simulation efficiency and realism.

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.