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

Chairi Kiourt, Vassilis Evangelidis, Dimitris Grigoropoulos

TL;DR

A multi-agent-based modeling framework for simulating archaeological mobility in uneven landscapes, integrating realistic terrain reconstruction, heterogeneous agent modeling, and adaptive navigation strategies is presented, enabling agents to respond efficiently to dynamic obstacles and interactions without costly global replanning.

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.

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

TL;DR

A multi-agent-based modeling framework for simulating archaeological mobility in uneven landscapes, integrating realistic terrain reconstruction, heterogeneous agent modeling, and adaptive navigation strategies is presented, enabling agents to respond efficiently to dynamic obstacles and interactions without costly global replanning.

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.
Paper Structure (27 sections, 8 equations, 7 figures, 4 tables)

This paper contains 27 sections, 8 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Mobility and Movement in Archaeological Landscapes
  4. Agent-Based Modeling of Entities
  5. Global Path Planning and Local Navigation Adaptation
  6. Terrain Representation for Spatial Analysis and Simulation
  7. Methodology
  8. Hybrid Navigation Framework
  9. Agents modeling
  10. Human-agent modeling
  11. Animal-Based Transport Agent Mobility
  12. From Real Elevation Maps to 3D Environment Reconstruction and Simulation
  13. Immersive Agent-Centric Visualization
  14. Use Cases
  15. Kimmeria: Terrain-Aware Pursuit and Evasion
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Illustration of the hybrid navigation strategy combining global A* path planning and local Q-learning adaptation.
  • Figure 2: Agent categories modeled in the simulation. Images were generated using AI based on structured scientific text descriptions and visual specifications. Depicted elements such as attire and pottery types were informed by established archaeological evidence and scholarly sources
  • Figure 3: Agent-centric immersive visualization showing the simulated environment from the viewpoint of an agent, enabling inspection of perception, visibility, and interaction dynamics in first-person perspective.
  • Figure 4: Left-image: The fort at Kimmeria in its broader landscape. Top-image: Remains of the defensive wall, Right-image:Viewshed analysis from the Roman-period fort of Kimmeria, illustrating terrain visibility within an approximate 6 km radius
  • Figure 5: The sanctuary at Kalpodi in its position in local networks (left). Local Harbours in relation to Kalapodi (right)
  • ...and 2 more figures