Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain

摘要

Most real world environments are hard to model completely and correctly, especially to model the dynamics of the environment. In this paper we present our work to improve a domain model through learning from execution, thereby improving a task planner's performance. Our system collects execution traces from the robot, and automatically extracts relevant information to improve the domain model. We introduce the concept of situation-dependent rules, where situational features are used to identify the conditions that affect action achievability. The system then converts this execution knowledge into a symbolic representation that the planner can use to generate plans appropriate for given situations. Introduction Most real world environments are hard to model completely and correctly. Regardless of the level of detail and the care taken to model the domain, systems are bound to encounter uncertainty and incomplete information. Advanced search techniques may mitigate some of...