Toward Meta-level Control of Autonomous Agents

AbstractMetareasoning is an important capability for autonomous systems, particularly for those being deployed on long duration missions. An agent with increased self-observation and the ability to control itself in response to changing environments will be more capable in achieving its goals. This is essential for long-duration missions where system designers will not be able to, theoretically or practically, predict all possible problems that the agent may encounter. In this paper we describe preliminary work that integrates the metacognitive architecture MIDCA with an autonomous TREX agent, creating a more self-observable and adaptive agent

Anomaly Detection for Symbolic Representations

A fully autonomous agent recognizes new problems, explains what causes such problems, and generates its own goals to solve these problems. Our approach to this goal-driven model of autonomy uses a methodology called the Note-Assess-Guide procedure. It instantiates a monitoring process in which an agent notes an anomaly in the world, assesses the nature and cause of that anomaly, and guides appropriate modifications to behavior. This report describes a novel approach to the note phase of that procedure. A-distance, a sliding-window statistical distance metric, is applied to numerical vector representations of intermediate states from plans generated for two symbolic domains. Using these representations, the metric is able to detect anomalous world states caused by restricting the actions available to the planner