A common problem encountered in many application scenarios is how to represent some prior knowledge about a set of possibilities in such a way that, regardless of which possibility materializes, we can quickly identify it. Put differently, we need to represent domain information in an efficiently navigable way to aid "diagnosis". The most common representation is perhaps a flowchart (or a decision tree). The problem with flowcharts is that they are notoriously difficult to maintain. Additional knowledge often has to be manually integrated s the system becomes more complex, making it impossible to keep track of all possible decision paths, let alone optimize the flow to maximize performance. This is a real problem faces by practitioners relying on the use of flowcharts. We propose an efficient method for optimizing an existing flowchart based on a conversion to an auxiliary matrix representation. The main goal of the paper is to show a synergy between the two representational in the hope that this will help practitioners choose a better strategy for their application. We show that such a conversion suggests ways for improving both representations - ways that were not envisioned when using each representation alone.
By: Alina Beygelzimer, Mark Brodie, Sheng Ma, Irina Rish
Published in: Proceedings of 9th IFIP/IEEE International Symposium on Integrated Network Management.Piscataway, NJ,, IEEE. , p.529-42 in 2005
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