基于多智能体的考古移动性在不规则地形中的模拟
Multi-Agent-Based Simulation of Archaeological Mobility in Uneven Landscapes
AI总结：
本文提出了一种多智能体建模框架，用于模拟不规则地形中的考古移动性，通过强化学习实现高效动态适应，展示了地形感知追捕和运输分析的应用。
摘要：
理解考古景观中的移动、运动和互动对于解读过去的人类行为、运输策略和空间组织至关重要，但仅凭静态考古证据难以重建这些过程。本文提出了一种多智能体建模框架，用于模拟不规则地形中的考古移动性，整合了真实的地形重建、异质智能体建模和自适应导航策略。所提出的方法结合了全局路径规划与局部动态适应，通过强化学习，使智能体能够高效响应动态障碍和互动，而无需昂贵的全局重新规划。实际的数字高程数据被处理成高保真的三维环境，保留坡度和地形约束，这些直接影智能体的移动。该框架明确建模了多样化的智能体类型，包括人类群体和基于动物的运输系统，每种类型都通过实证基础的移动特征参数化，如负载、坡度耐受性和物理尺寸。两个考古启发的使用案例展示了该方法的应用性：一个地形感知的追捕和逃避场景和一个涉及驮兽和轮式车辆的比较运输分析。结果突显了地形形态、可见性和智能体异质性对移动结果的影响，而提出的混合导航策略为大规模、动态的考古模拟提供了一种计算高效且可解释的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
