Enhancing multi-agent system testing with diversity-guided exploration and adaptive critical state exploitation

• Main Authors: MA, Xuyan, WANG, Yawen, WANG, Junjie, XIE, Xiaofei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Adaptive perturbation exploitation; Critical-state; Diversity-guided exploration; Failure scenarios; Intelligent transportation; Multi-agent system testing; Multiagent systems (MASs); Multirobots; Robots control; System testing; Robotics; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9443; https://ink.library.smu.edu.sg/context/sis_research/article/10443/viewcontent/Enhancing_Multi_agent_System_Testing_with_Diversity_Guided_Exploration_and_Adaptive_Critical_State_Exploitation.pdf
• Institution: Singapore Management University

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this paper, to highlight a typical balance between exploration (diversifying behaviors) and exploitation (detecting failures), we propose an advanced testing framework for MAS called with diversity-guided exploration and adaptive critical state exploitation. It incorporates both individual diversity and team diversity, and designs an adaptive perturbation mechanism to perturb the action at the critical states, so as to trigger more and more diverse failure scenarios of the system. We evaluate MASTest on two popular MAS simulation environments: Coop Navi and StarCraft II. Results show that the average distance of the resulting failure scenarios is increased by 29.55%-103.57% and 74.07%-370.00% on two environments compared to the baselines. Also, the failure patterns found by MASTest are improved by 71.44%-300.00% and 50%-500.00% on two experimental environments compared to the baselines.