Multi-agent systems are designed to deal with open, distributed systems with unpredictable dynamics, which makes them inherently hard to test. The value of using simulation for this purpose is recognized in the literature, although achieving sufficient fidelity (i.e., the degree of similarity between the simulation and the real-world system) remains a challenging task. This is exacerbated when dealing with cognitive agent models, such as the Belief Desire Intention (BDI) model, where the agent codebase is not suitable to run unchanged in simulation environments, thus increasing the reality gap between the deployed and simulated systems. We argue that BDI developers should be able to test in simulation the same specification that will be later deployed, with no surrogate representations. Thus, in this paper, we discuss how the control flow of BDI agents can be mapped onto a Discrete Event Simulation (DES), showing that such integration is possible at different degrees of granularity. We substantiate our claims by producing an open-source prototype integration between two pre-existing tools (JaKtA and Alchemist), showing that it is possible to produce a simulation-based testing environment for distributed BDI} agents, and that different granularities in mapping BDI agents over DESs may lead to different degrees of fidelity.

翻译：多智能体系统旨在处理具有不可预测动态特性的开放分布式系统，这使得其测试工作具有内在困难性。尽管学界已认识到利用仿真技术进行测试的价值，但实现足够的保真度（即仿真系统与现实系统之间的相似程度）仍然是一项具有挑战性的任务。当处理认知智能体模型（如信念-愿望-意图模型）时，这一问题尤为突出：由于智能体代码库无法在仿真环境中直接运行，导致部署系统与仿真系统之间的现实差距进一步扩大。我们认为BDI开发者应当能够在仿真环境中测试与后续部署完全相同的系统规范，而无需采用替代性表示方法。为此，本文探讨了如何将BDI智能体的控制流映射到离散事件仿真框架中，并证明这种集成可以在不同粒度级别上实现。我们通过整合两个现有工具（JaKtA与Alchemist）开发了开源原型系统，验证了为分布式BDI智能体构建基于仿真的测试环境的可行性，同时揭示了不同粒度的BDI到DES映射方式可能导致不同程度的仿真保真度。