POIROT: acquiring workflows by combining models learned from interpreted traces

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. Categories and Subject Descriptors Learning, knowledge acquisition. analogies, planning, procedures. General Terms Algorithms, Performance, Design Keywor...

• Type: Conference
• Year: 2009
• Where: KCAP
• Authors: Mark H. Burstein, Fusun Yaman, Robert Laddaga, Robert J. Bobrow
• Keywords: Explicit Learning Hypotheses, Information Retrieval, Internal Learning Subtasks, KCAP 2009, Trace Interpretation