Automated Ethical Prioritization in Autonomous Emergency Response Systems via Bayesian Network Optimization

**Abstract:** This paper introduces a novel approach to automating ethical decision-making in Autonomous Emergency Response Systems (AERS). Focusing on the sub-field of *resource allocation under scarcity in triage scenarios* within the broader 도덕적 딜레마 상황에서의 다중 기준 의사결정 프레임워크 domain, we present a Bayesian Network (BN) optimization framework that dynamically prioritizes patients based on a combination of quantifiable medical criteria and ethically-weighted subjective factors. Our system, incorporating a reinforcement learning (RL) feedback loop, demonstrably outperforms traditional triage algorithms by minimizing overall societal harm and maximizing equitable resource utilization, offering a pathway for the responsible deployment of AERS and significantly improving patient outcomes in crisis situations. The core innovation lies in the adaptive assessment of ethical weights via continuous RL, mitigating biases inherent in pre-defined ethical guidelines and enhancing resilience to evolving societal values.