Poirot: Acquiring Workflows By Combining Models Learned From Interpreted Traces
K-CAP(2009)
摘要
The POIROT project is a four-year effort to develop an architecture that integrates the products of a number of targeted reasoning and learning components to produce executable representations of demonstrated web service workflow processes. To do this it combines contributions from multiple trace analysis (interpretation) and learning methods guided by a meta-control regime that reviews explicit learning hypotheses and posts new learning goals and internal learning subtasks. POIROT's meta-controller guides the activity of its components through largely distinct phases of processing from trace interpretation, to inductive learning, hypotheses combination and experimental evaluation In this paper we discuss the impact that various kinds of inference during the trace interpretation phase can have on the quality of the learned models.
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关键词
Multi-strategy learning,meta-control,integration
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