  The study of databases belongs to computer science, not specifically to AI. But one of the research paradigms in the scientific analysis of databases uses logical models of the representations and reasoning  (see  Minker 1997  for a recent survey of the field), and this area has interacted with logical AI. The deductive database paradigm was taking shape at about the same time that many AI researchers were thinking through the problems of nonmonotonic logic, and provided several specific examples of nonmonotonic reasoning that called for analyses. Of these, perhaps the most important is the closed-world assumption, according to which—at least as far as simple facts are concerned, represented in the database as positive or negative literals—the system assumes that it knows all that there is to be known. It is the closed world assumption that justifies a negative answer to a query “Is there a direct flight from Detroit to Bologna?” when the system finds no such flight in its data. This is another case of inference from the absence of a proof; a negative is proved, in effect, by the failure of a systematic attempt to prove the positive. This idea, which was investigated in papers such as  Reiter 1978  and  Clark 1978  also provided a challenge for nonmonotonic logics, as well as specific intuitions—note that again, the idea of inference rules depending on the absence of a proof is present here. 