Lenat, Douglas B.


CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks

AI Magazine

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.



Heuristic Search for New Microcircuit Structures: An Application of Artificial Intelligence

AI Magazine

Three experiments have been conducted, and some novel designs and design rules have emerged. The paradigm for Eurisko's exploration is a loop in which it generates a new device configuration, computes its I/O behavior, tries to "parse" this into a functionally it already knows about and can use, and then evaluates the results. In the first experiment, this loop took place at the level of charged carriers moving under the effects of electric fields through abutted regions of doped and undoped semiconductors. This was unsurprising, as they were short sentences in the descriptive language we had defined (a language with verbs like Abut and ApplyEField, and with nouns like nDoped Region and IntrinsicChannellRegion).


The ubiquity of discovery

Classics

See also: IJCAI-77 paperArtificial Intelligence, 9[#3]:257-285


AM: an Artificial Intelligence approach to Discovery in Mathematics as Heuristic Search

Classics

Ph.D. Dissertation. Stanford AI Laboratory. Reprinted as AM: Discovery in Mathematics as Heuristic Search, in Randall Davis and Douglas B Lenat (eds.), Knowledge-Based Systems in Artificial Intelligence, pp. 1-225, New York: McGraw-Hill (1982).Stanford AI Laboratory