Proceedings of the AAAI Conference on Artificial Intelligence, 21
Reasoning about Plans and Actions
The use of embedded technology has become widespread. Many complex engineered systems comprise embedded features to perform self-diagnosis or self-reconfiguration. These features require fast response times in order to be useful in domains where embedded systems are typically deployed. Researchers often advocate the use of compilation-based approaches to store the set of environments (resp. solutions) to a diagnosis (resp. reconfiguration) problem, in some compact representation. However, the size of a compiled representation may be exponential in the treewidth of the problem. In this paper we propose a novel method for compiling the most preferred environments in order to reduce the large space requirements of our compiled representation. We show that approximate compilation is an effective means of generating the highest-valued environments, while obtaining a representation whose size can be tailored to any embedded application. The method also provides a graceful way to tradeoff space requirements with the completeness of our coverage of the environment space.