The Challenge of Partial Grounding in Constraint Compliance

Autonomous AI systems that act and interact in human environments must generally comply with systems of rules, laws, norms, and moral principles that humans are expected to abide by. One of the core challenges of maintaining compliance with such systems of constraints is that the current situation (as perceived and experienced by an agent) may be insufficient for immediately determining whether the agent is in compliance with any one of its (often many) individual constraints. We term this problem partial grounding of constraint specifications: in many situations, an agent will only be able to partially ground its evaluation of constraints. We define this problem, describe some requirements for mitigating it, and present a specific computational approach. The approach enables an agent to anticipate potential grounding issues when constraints are introduced. The anticipatory processing then allows an agent to respond more quickly to novel instances of partially-grounded constraint specifications when/as they arise in execution. The approach is assessed in the context of an analytic tool proposed to help researchers compare and evaluate approaches to grounding in intelligent systems.