Abstract: Effective disaster management demands rapid coordination between heterogeneous agents tasked with search, rescue, and debris clearance in dynamic environments. Traditional simulation tools often lack flexibility, contextual awareness, and scalability, limiting their use in evaluating multi-agent cooperation under realistic conditions. This paper introduces MAS-SDM (Multi-Agent Simulation Sandbox for Disaster Management), an intelligent, tile-based simulation framework designed to model and analyze autonomous agent behaviour within disaster zones. Built on a 10×10 grid environment created using the Tiled Map Editor, the system simulates a constrained yet richly interactive disaster landscape featuring survivors, debris, safe zones, and obstacles distributed across layered terrain. The sandbox employs four cooperative agents—two specialized in survivor rescue and two in debris removal—each governed by rule-based or reinforcement learning policies that enable dynamic decision-making and task prioritization. Through real-time visualization powered by the Python Pygame engine, MAS-SDM provides an experimental platform for evaluating agent efficiency, coordination strategies, and environment adaptability. Beyond simulating immediate response scenarios, the framework serves as a foundation for developing scalable, data-driven models in multi-agent reinforcement learning (MARL) and disaster logistics optimization. Future work will extend the simulation to larger maps, introduce adaptive communication between agents, and integrate learning modules to autonomously improve cooperative performance in complex, evolving disaster environments.

Keywords: Multi-Agent Systems, Disaster Management, Simulation Sandbox, Tile-based Environment, Reinforcement Learning, Cooperative Agents, Search and Rescue, Debris Clearance, Tiled Map Editor, Pygame

Downloads: | DOI: 10.17148/IJIREEICE.2025.131034