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This paper describes the implementation of a typical data-base manaer for an A.I. language like Planner, Conniver, or QA4, and some proposed extensions for applications involving greater quantities of data than usual. The extensions are concerned with data bases involving several active and potentially active sub-data-bases, or "contexts". The major mechanisms discussed are the use of contexts as packets of data with free variables; and indexing data according to the contexts they appear in. The paper also defends the Planner approach to data representations against some more recent proposals. |
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