Software safety: why, what, and how
ACM Computing Surveys (CSUR)
A methodology for knowledge acquisition and reasoning in failure analysis of systems
IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
On building systems that will fail
Communications of the ACM - Special issue on LISP
Constructive interpretation of human-generated exceptions during plan execution
Constructive interpretation of human-generated exceptions during plan execution
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Accepting the inevitable: the role of failure recovery in the design of planners
Accepting the inevitable: the role of failure recovery in the design of planners
A Computational Model of Skill Acquisition
A Computational Model of Skill Acquisition
Error recovery in robot systems.
Error recovery in robot systems.
Case-based planning: an integrated theory of planning, learning and memory
Case-based planning: an integrated theory of planning, learning and memory
An Architecture for Persistent Reactive Behavior
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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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驴 potential use on real world applications). This 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.