Artificial Intelligence
On the computational complexity of temporal projection, planning, and plan validation
Artificial Intelligence
Plan evaluation with incomplete action descriptions
Eighteenth national conference on Artificial intelligence
The Object Constraint Language: Getting Your Models Ready for MDA
The Object Constraint Language: Getting Your Models Ready for MDA
Analysis of interacting BPEL web services
Proceedings of the 13th international conference on World Wide Web
Bringing Planning to Autonomic Applications with ABLE
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
The Role of Visual Modeling and Model Transformations in Business-driven Development
Electronic Notes in Theoretical Computer Science (ENTCS)
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Domain independent approaches for finding diverse plans
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Changerefinery: assisted refinement of high-level IT change requests
POLICY'09 Proceedings of the 10th IEEE international conference on Policies for distributed systems and networks
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The scalability of recent planning algorithms allows developers to automate planning tasks, which so far have been reserved to humans. However in real-world applications, synthesizing a plan is just the beginning of a complex life-cycle management process. Plans must be organized in large collections, where they can be grouped along different purposes and are amenable to the search, inspection, evaluation, and modification by human experts or automated reasoning systems. Eventually, plans will outlast their utility and be replaced. We present our solution to plan life cycle management for an autonomic computing application. We focus in particular on the automatic synthesis of plan metadata for plans containing conditional and parallel actions, well-structured loops, and non-deterministic choices. The plans are of unknown origin, i.e., their underlying action model. which could provide us with pre- and postconditions, is not available. New analysis techniques are presented that uniformly generate metadata for plans, thus allowing a system to embed plans into context and organize them in meaningfully structured plan repositories.