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

Researchers developed a multi-agent simulation framework using reinforcement learning to model archaeological mobility patterns in complex terrain. The system combines global path planning with local adaptation to simulate human and animal movement in historical landscapes, demonstrated through pursuit scenarios and transport analysis.