Goal-driven autonomy with semantically-annotated hierarchical cases

Abstract

We present LUiGi-H a goal-driven autonomy (GDA) agent. Like other GDA agents it introspectively reasons about its own expectations to formulate new goals. Unlike other GDA agents, LUiGi-H uses cases consisting of hierarchical plans and semantic annotations of the expectations of those plans. Expectations indicate conditions that must be true when parts of the plan are executed. Using an ontology, semantic annotations are defined via inferred facts enabling LUiGi- H to reason with GDA elements at different levels of abstraction. We compared LUiGi-H against an ablated version, LUiGi, that uses nonhierarchal cases. Both agents have access to the same base-level (i.e. non-hierarchical plans), while only LUiGi-H makes use of hierarchical plans. In our experiments, LUiGi-H outperforms LUiGi.