Domain-Independent Heuristics for Goal Formulation.

摘要

Goal-driven autonomy is a framework for intelligent agents that automatically formulate and manage goals in dynamic environments, where goal formulation is the task of identifying goals that the agent should attempt to achieve. We argue that goal formulation is central to high-level autonomy, and explain why identifying domain-independent heuristics for this task is an important research topic in high-level control. We describe two novel domain- independent heuristics for goal formulation (motivators) that evaluate the utility of goals based on the projected consequences of achieving them. We then describe their integration in M-ARTUE an agent that balances the satisfaction of internal needs with the achievement of goals introduced externally. We assess its performance in a series of experiments in the Rovers With Compass domain. Our results show that using domain-independent heuristics yields performance comparable to using domain-specific knowledge for goal formulation. Finally, in ablation studies we demonstrate that each motivator contributes significantly to M-ARTUE's performance.