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.


Eurisko: A Program Which Learns New Heuristics and Domain Concepts

Classics

In essence, AM was an automatic programming system, whose primitive actions were modifications to pieces of LISP code, code which represented the characteristic functions of various math concepts. It was only because of the deep relationship between LISP and Mathematics that these operations (loop unwinding, recursion elimination, composition, argument elimination, function substitution, etc.) But no such deep relationship existed between LISP and Heuristics, and when the basic automatic programming operators were applied to viable, useful heuristics, they almost always produced useless (often worse than useless) new rules. Along the way, some very powerful new concepts, designs, and heuristics were indeed discovered mechanically.


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

As scientists interested in studying the phenomenon of "intelligence", we first choose a view of Man, develop a theory of how intelligent behavior is managed, and construct some models which can test and refine that theory. Among them is AM, a computer program that develops new mathematical concepts and formulates conjectures involving them; AM is guided in this exploration by a collection of 250 more or less general heuristic rules. The operational nature of such models allows experiments to be performed upon them, experiments which help us test and develop hypotheses about intelligence. One interesting finding has been the ubiquity of this kind of heuristic guidance: intelligence permeates everyday problem solving and invention, as well as the kind of problem solving and invention that scientists and artists perform.


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

Classics

The local heuristics communicate via an agenda mechanism, a global list of tasks for the system to perform and reasons why each task is plausible. Repeatedly, the program selects from the agenda the task having the best supporting reasons, and then executes it. Each concept is an active, structured knowledge module. A hundred very incomplete modules are initially provided, each one corresponding to an elementary set-theoretic concept (e.g.,union).