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Constructing plans that can handle multiple problem instances is a longstanding open problem in AI.
Research on multiple problem behaviors has focused on the concept of Problem Behavior Syndrome (PBS).
Multiple problem formulations are solved that use alternative objective functions and allow varying degrees of freedom in the decision space.
The uncertainty that multiple problem solvers bring with them is its own form of naturally emerging diversity, which provides rich ground for adaptation.
The success of later end-user software engineering (EUSE) activities largely depends on how many components each tool has and how adaptable components are to multiple problem domains.
The work discussed in this paper is mainly consolidated of two independently targeted frameworks: a data life cycle management framework for e-Science applications and a code execution framework handling multiple problem solving environments.
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