A Cross-domain Concept Formation System : Rhea

Abstract

In this paper we present a machine learning system Rhea. Rhea is characterized as a cross-domain concept formation system that obtains concepts from tuples of instances from multiple domains. Traditionally, concept formation is modeled in single domains. However, concepts should be formed with their use in mind. For example, the cancept of "chair" cannot be formed completely without the reasoner having a goal to sit on it. This task can be viewed as a cross-domain concept formation between form and function (goal). When learning from n domains, Rhea accepts n-tuple inputs. Each fragment of the input is an instance from one domain. Rhea has a conjecture that some rules constrain the possible combinations of instances from different domains and tries to find and/or generalize the rules. We implement Rhea in the domains of natural language expressions and outer-world descriptions. The implementation can acquire (1) extensions to the representation language for natural language expressions, which are syntactic rules that parse input expressions, (2) rules that give an account for relations between the domains, which can be interpreted as "meaning" of language expressions and classes of language expressions, and (3) classes each rule can apply, which are categorization of the input language expressions. To sum up, the implementation can be seen as a unified model of syntax and semantics acquisition based on general learning mechanism and shows learning from two domains is essential to classifications and finding rules.