This paper presents a logic programming-based framework for policy-aware autonomous agents that can reason about potential penalties for non-compliance and act accordingly. While prior work has primarily focused on ensuring compliance, our approach considers scenarios where deviating from policies may be necessary to achieve high-stakes goals. Additionally, modeling non-compliant behavior can assist policymakers by simulating realistic human decision-making. Our framework extends Gelfond and Lobo's Authorization and Obligation Policy Language (AOPL) to incorporate penalties and integrates Answer Set Programming (ASP) for reasoning. Compared to previous approaches, our method ensures well-formed policies, accounts for policy priorities, and enhances explainability by explicitly identifying rule violations and their consequences. Building on the work of Harders and Inclezan, we introduce penalty-based reasoning to distinguish between non-compliant plans, prioritizing those with minimal repercussions. To support this, we develop an automated translation from the extended AOPL into ASP and refine ASP-based planning algorithms to account for incurred penalties. Experiments in two domains demonstrate that our framework generates higher-quality plans that avoid harmful actions while, in some cases, also improving computational efficiency. These findings underscore its potential for enhancing autonomous decision-making and informing policy refinement. Under consideration in Theory and Practice of Logic Programming (TPLP).

翻译：本文提出了一种基于逻辑编程的政策感知自主智能体框架，该框架能够推理不合规行为可能带来的惩罚并据此行动。先前的研究主要侧重于确保合规性，而我们的方法考虑了在某些高风险目标下偏离政策可能是必要的情形。此外，通过模拟现实中的人类决策过程，对不合规行为进行建模有助于政策制定者优化政策设计。本框架扩展了Gelfond与Lobo的授权与义务策略语言（AOPL），以纳入惩罚机制，并集成答案集编程（ASP）进行推理。相较于既有方法，我们的方法能确保策略的规范性，考虑策略优先级，并通过明确识别规则违反及其后果来增强可解释性。基于Harders和Inclezan的研究，我们引入了基于惩罚的推理机制，以区分不同的不合规计划，并优先选择影响最小的方案。为此，我们开发了从扩展AOPL到ASP的自动转换方法，并改进了基于ASP的规划算法以计入已发生的惩罚。在两个领域的实验表明，本框架能够生成更高质量的计划，避免有害行为，并在某些情况下提升计算效率。这些发现凸显了该框架在增强自主决策能力及优化政策制定方面的潜力。本文已提交至《逻辑编程理论与实践》（TPLP）期刊审议。