Oruga: An Avatar of Representational Systems Theory
This work aims to make machines more compatible with human use by addressing the challenge of flexible representation handling, though it appears incremental as it builds on prior theoretical work.
The paper tackles the problem of enabling machines to flexibly use and transform representations like humans do, by presenting Oruga, an implementation of Representational Systems Theory that includes data structures, a language, and an engine for structure transfer transformations.
Humans use representations flexibly. We draw diagrams, change representations and exploit creative analogies across different domains. We want to harness this kind of power and endow machines with it to make them more compatible with human use. Previously we developed Representational Systems Theory (RST) to study the structure and transformations of representations. In this paper we present Oruga (caterpillar in Spanish; a symbol of transformation), an implementation of various aspects of RST. Oruga consists of a core of data structures corresponding to concepts in RST, a language for communicating with the core, and an engine for producing transformations using a method we call structure transfer. In this paper we present an overview of the core and language of Oruga, with a brief example of the kind of transformation that structure transfer can execute.