Research and Advances
Artificial Intelligence and Machine Learning

Extending the infomation theory approach to converting limited-entry decision tables to computer programs


This paper modifies an earlier algorithm for converting decision tables into flowcharts which minimize subsequent execution time when compiled into a computer program. The algorithms considered in this paper perform limited search and, accordingly, do not necessarily result in globally optimal solutions. However, the greater search effort needed to obtain a globally optimal solution for complex decision tables is usually not justified by sufficient savings in execution time. There is an analogy between the problem of converting decision tables into efficient flowcharts and the well-understood problem in information theory of noiseless coding. The results of the noiseless coding literature are used to explore the limitations of algorithms used to solve the decision table problem. The analogy between the two problems is also used to develop improvements to the information algorithm in extending the depth of search under certain conditions and in proposing additional conditions to be added to the decision table. Finally, the information algorithm is compared with an algorithm proposed in a recent paper by Verhelst.

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