On Reasoning DAVID WALTZ NEC Research Institute, From Data l%nceton, NJ ,, waltz(fl research .nj. nee. com ) SIMON Department ! KASIF of Computer eda) ScLence Johns Hopkins Unwersltv, Baltlmore, MD k;[~lf(o CSJILU Introduction Our society is currently entering a new phase in which gigabytes of information are becoming readily available for exploration over academic networks, digital libraries, and commercial information services as well as in proprietary commercial and governmental databases. This important technological development presents a substantial challenge, as future intelligent systems must be able to store very large streams of data, summarize and index this data using concise and efficient models, and subsequently perform very efficient retrieval and reasoning in response to real-time queries and updates. We informally refer to this challenging task as reasoning from data. Most previous AI research and applications have concentrated on relatively simple operations, for example, highly constrained queries on relatively static, immutable systems of knowledge such as mathematics, chess, and hardware components inventories, where it is possible to abstract rules that can be viewed as true and valid. There are many other domains in which data changes more or less rapidly and in which abstract truths are at best
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On reasoning from data
Waltz, David; Kasif, Simon
Follow ACM Computing Surveys (CSUR)
, Volume 27 (3)
Association for Computing Machinery – Sep 1, 1995
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