Functional transformations in AI discovery systems

Abstract

The power of scientific discovery systems derives from two main sources: a set of heuristics that determines when to apply a creative operator (an operator for forming new operators and concepts) in a space that is being explored; and a set of creative operators that determines what new operators and concepts will be created for that exploration. This paper is mainly concerned with the second issue. A mechanism called functional transformation (FT) shows promising power in creating new and useful creative operators during exploration. This paper discusses the definition, creation, and application of functional transformations, and describes, as a demonstration of the power of FT, how the system ARE, starting with a small set of creative operations and a small set of heuristics, uses FTs to create all the concepts attained by Lenat's AM system [4], and others as well. Besides showing an alternative way, of Lenat's eurisko [5], to meet the criticisms of too much pre-programmed knowledge [6] that have been leveled against AM, ARE provides a route to discovery systems that are capable of “refreshing” themselves indefinitely by continually creating new operators.