Improving the reliability of artificial intelligence planning systems by analyzing their failure recovery

As planning technology improves, artificial intelligence planners are being embedded in increasingly complicated environments: ones that are particularly challenging even for human experts. Consequently, failure is becoming both increasingly likely for these systems (due to the difficult and dynamic nature of the new environments) and increasingly important to address (due to the systems' potential use on real world applications). The paper describes the development of a failure recovery component for a planner in a complex simulated environment and a procedure (called failure recovery analysis) for assisting programmers in debugging that planner. The failure recovery design is iteratively enhanced and evaluated in a series of experiments. Failure recovery analysis is described and demonstrated on an example from the Phoenix planner. The primary advantage of these approaches over existing approaches is that they are based on only a weak model of the planner and its environment, which makes them most suitable when the planner is being developed. By integrating them, failure recovery and failure recovery analysis improve the reliability of the planner by repairing failures during execution and identifying failures due to bugs in the planner and failure recovery itself.<
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