Scalability in Modeling and Simulation Systems for Multi-Agent, AI, and Machine Learning Applications

摘要

Today's battlefield increasingly incorporates emerging technologies using artificial intelligence. These systems not only provide unparalleled speed and accuracy, but also allow for digital models to be developed and tested in simulation prior to deployment, reducing the time and cost of acquisition. This holds additional promise for wargaming modeling and simulation for understanding the impact of complex, multi-domain operations on future force efficacy and structure. However, current modeling and simulation environments are not designed for simulating decentralized, intelligent systems at scale. Cloud computing has revolutionized how we scale computational capability, but was not designed for complex, low latency interactions between independently reasoning entities. This motivates new methods for characterizing and mitigating complexity to meet operational and mission requirements. We outline the challenges and opportunities for modeling and simulating large-scale multi-agent systems and identify future research areas that should address these challenges. We recommend that investment be placed in holistically understanding scalability from a cost-benefit perspective, measuring the impact on requirements, developing improved tools for understanding the dimensions of scalability, and formalizing specifications of the scalability requirements met (or not met) by available systems. We propose that a framework for reasoning over and adjusting the fidelity of various models within a system of systems is needed to meet development and testing requirements. Formal methods can be used to understand the limits on scalability as a function of objectives (e.g. speed, convergence, performance) and constraints (e.g. cost, compute, and time), optimizing resources to develop and test interacting artificial intelligence systems at scale.