The Learning Shell: Automated Macro Construction
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By analysing sequences of actions performed by a user, one can find frequent subsequences that can be suggested as macro (script) definitions. However, often these ‘actions’ have additional features. In this paper we combine an algorithm to detect frequent subsequences with an inductive logic programming system to automatically generate for each frequent subsequence the most specific ‘template’ for these additional features that is consistent with the observed frequent subsequences. The resulting system is implemented and used in an application where we automatically generate macros from logs of the use of a Unix command shell.
Keywordsmachine learning inductive logic programming interface adaptation
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