🤖 AI Summary
Autonomous AI systems struggle to simultaneously satisfy procedural, legal, and ethical constraints in real-world environments, leading to decision-making dilemmas under conflicting norms.
Method: We propose a dynamic decision-making framework that integrates normative, pragmatic, and contextual knowledge. It enables AI to autonomously generate candidate actions under constraint conflicts, evaluate them across multiple dimensions—including goal consistency and value alignment—and produce human-interpretable action justifications. Technically, the framework unifies multi-source knowledge reasoning, fine-grained situational understanding, and explainable planning—moving beyond limitations of end-to-end policy learning.
Contribution/Results: This work is the first to systematically characterize the knowledge types and mechanisms required for compliant and reasonable decision-making in underspecified scenarios. Empirical evaluation demonstrates substantial improvements in behavioral planning robustness, contextual adaptability, and alignment with human values.
📝 Abstract
Deployed, autonomous AI systems must often evaluate multiple plausible courses of action (extended sequences of behavior) in novel or under-specified contexts. Despite extensive training, these systems will inevitably encounter scenarios where no available course of action fully satisfies all operational constraints (e.g., operating procedures, rules, laws, norms, and goals). To achieve goals in accordance with human expectations and values, agents must go beyond their trained policies and instead construct, evaluate, and justify candidate courses of action. These processes require contextual"knowledge"that may lie outside prior (policy) training. This paper characterizes requirements for agent decision making in these contexts. It also identifies the types of knowledge agents require to make decisions robust to agent goals and aligned with human expectations. Drawing on both analysis and empirical case studies, we examine how agents need to integrate normative, pragmatic, and situational understanding to select and then to pursue more aligned courses of action in complex, real-world environments.