2024/10/26 by Luyao Niu, Hongchao Zhang, Niu, Luyao +9
Computer Science · Engineering · Social Sciences · #Ethics and Social Impacts of AI #FOS: Electrical engineering #Information and Cyber Security #Smart Grid Security and Resilience #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.20288
openalex publication_date 2024/10/26 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28
Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environments or due to incorrect actions taken by an agent. When an erroneous decision that leads to a violation of safety is identified, assigning responsibility to individual agents is a key step toward preventing future accidents. Current approaches to carrying out such investigations require human labor or high degree of familiarity with operating environments. Automated strategies to assign responsibility can achieve a significant reduction in human effort and associated cognitive burden. In this paper, we develop an automated procedure to assign responsibility for safety violations to actions of any single agent in a principled manner. We base our approach on reasoning about safety violations in road safety. Given a safety violation, we use counterfactual reasoning to create alternative scenarios, showing how different outcomes could have occurred if certain actions had been replaced by others. We introduce the degree of responsibility (DoR) metric for each agent. The DoR, using the Shapley value, quantifies each agent's contribution to the safety violation, providing a basis to explain and justify decisions. We also develop heuristic techniques and methods based on agent interaction structures to improve scalability as agent numbers grow. We examine three safety violation cases from the National Highway Traffic Safety Administration (NHTSA). We run experiments using CARLA urban driving simulator. Results show the DoR improves the explainability of decisions and accountability for agent actions and their consequences.