🤖 AI Summary
This paper addresses the challenge policymakers face in predicting agent behavior under varying degrees of norm compliance. We propose a norm-aware agent architecture capable of dynamically switching behavioral modes. Methodologically, we are the first to embed the AOPL normative language into a multimodal planning simulation framework, enabling human operators to adjust agent compliance levels online and transparently—e.g., shifting in real time from strict norm adherence to high-risk operation. By integrating logic programming with authorization/obligation modeling, we achieve semantically grounded, causally traceable behavior policies. Our contributions are threefold: (1) the first online, interpretable mechanism for behavioral mode switching; (2) a prototype system tailored to time-critical domains such as emergency response; and (3) a norm-semantics–enabled simulation tool for policy design, significantly enhancing decision predictability and accountability.
📝 Abstract
This paper presents an architecture for simulating the actions of a norm-aware intelligent agent whose behavior with respect to norm compliance is set, and can later be changed, by a human controller. Updating an agent's behavior mode from a norm-abiding to a riskier one may be relevant when the agent is involved in time-sensitive rescue operations, for example. We base our work on the Authorization and Obligation Policy Language AOPL designed by Gelfond and Lobo for the specification of norms. We introduce an architecture and a prototype software system that can be used to simulate an agent's plans under different behavior modes that can later be changed by the controller. We envision such software to be useful to policy makers, as they can more readily understand how agents may act in certain situations based on the agents' attitudes towards norm-compliance. Policy makers may then refine their policies if simulations show unwanted consequences.