GAMMS: Graph based Adversarial Multiagent Modeling Simulator

2026-02-04

AI Summary

This work proposes a lightweight and scalable graph-based multi-agent simulation framework to address the high computational cost of existing high-fidelity simulators, which hinders rapid prototyping and large-scale deployment. The framework adopts a graph-centric, integration-first architecture that efficiently supports the simulation and evaluation of heuristic, optimization-based, and learning-driven strategies—including large language models—within complex graph-structured environments. It is compatible with mainstream machine learning libraries and planning solvers, enabling efficient execution on commodity hardware for real-world scenarios such as urban road networks. Furthermore, the framework provides out-of-the-box visualization capabilities, significantly lowering the barrier to entry for multi-agent systems research and accelerating progress in collaborative decision-making, autonomous planning, and adversarial modeling.

Technology Category

• Multiagent Systems: Multiagent Planning
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

As intelligent systems and multi-agent coordination become increasingly central to real-world applications, there is a growing need for simulation tools that are both scalable and accessible. Existing high-fidelity simulators, while powerful, are often computationally expensive and ill-suited for rapid prototyping or large-scale agent deployments. We present GAMMS (Graph based Adversarial Multiagent Modeling Simulator), a lightweight yet extensible simulation framework designed to support fast development and evaluation of agent behavior in environments that can be represented as graphs. GAMMS emphasizes five core objectives: scalability, ease of use, integration-first architecture, fast visualization feedback, and real-world grounding. It enables efficient simulation of complex domains such as urban road networks and communication systems, supports integration with external tools (e.g., machine learning libraries, planning solvers), and provides built-in visualization with minimal configuration. GAMMS is agnostic to policy type, supporting heuristic, optimization-based, and learning-based agents, including those using large language models. By lowering the barrier to entry for researchers and enabling high-performance simulations on standard hardware, GAMMS facilitates experimentation and innovation in multi-agent systems, autonomous planning, and adversarial modeling. The framework is open-source and available at https://github.com/GAMMSim/GAMMS/

Problem

• multi-agent simulation
• scalability
• graph-based environments
• adversarial modeling
• rapid prototyping

Innovation

• graph-based simulation
• multi-agent systems
• adversarial modeling
• lightweight framework
• integration-first architecture