Metacat: A Self-Watching Cognitive Architecture for Analogy-Making

Metacat: A Self-Watching Cognitive Architecture for Analogy-Making James B. Marshall (marshall@cs.pomona.edu) Computer Science Program Pomona College 610 N. College Ave. Claremont, CA 91711 USA Abstract This paper describes Metacat, an extension of the Copycat analogy-making program. Metacat is able to monitor its own processing, allowing it to recog- nize, remember, and recall patterns that occur in its “train of thought” as it makes analogies. This gives the program a high degree of flexibility and self-control. The architecture of the program is de- scribed, along with a sample run illustrating the program’s behavior. Introduction This paper describes Metacat, an extension of the Copycat analogy-making program originally devel- oped by Hofstadter and Mitchell (Hofstadter, 1984; Mitchell, 1993). Copycat was developed as a model of the complex interplay between bottom-up and top-down perceptual processes in the mind, which together enable humans to perceive analogies be- tween diﬀerent situations in remarkably flexible ways. The program operates in an idealized mi- croworld of analogy problems involving short strings of letters. Although the program understands only a limited set of concepts pertaining to its letter-string world, its “fluid” processing mechanisms give it con- siderable flexibility in recognizing and applying these concepts in many diverse situations. The long-term goal of the Copycat line of research is to computationally model how high-level cognitive phenomena such as creativity, analogical perception, understanding, and self-awareness can arise out of a subcognitive substrate composed of a huge number of tiny, nondeterministic processes, each of which is far too small by itself to support such phenomena. Few people would suggest that individual neurons in the brain (or individual molecules) are “conscious” in anything like the normal sense in which humans experience consciousness. One is forced to accept the fact that self-awareness arises, somehow, out of nothing but billions of molecular chemical reactions and neuronal firings. How can individually meaning- less physical events in a brain—even a huge number of them—ultimately give rise to meaningful aware- ness? Hofstadter has argued that two key ideas are of paramount importance (Hofstadter and FARG, What seems to make brains conscious is the special way they are organized —in particular, the higher-level structures and mechanisms that come into being. I see two dimensions as being critical: (1) the fact that brains possess con- cepts, allowing complex representational struc- tures to be built that automatically come with associative links to all sorts of prior experiences, and (2) the fact that brains can self-monitor, allowing a complex internal self-model to arise, allowing the system an enormous degree of self- control and open-endedness. Work on Copycat explored the first idea through the development of a computer model of analogy- making in which the program’s representation of concepts is intimately intertwined with its mecha- nisms for perceiving similarity between diﬀerent ide- alized situations. Recent work has focused on the second idea by incorporating self-watching into the model—namely, the ability of a system to perceive and to explicitly characterize its own perceptual pro- cesses. The objective of this work has been to de- velop mechanisms that allow the program to monitor its own actions and to make explicit the ideas that come into play during the course of solving analogy problems. This can be thought of as adding a higher “cognitive” layer on top of the program’s “subcog- nitive” layer, enabling the program to watch and remember what happens at its subcognitive level as perceptual structures are built, reconfigured, and de- stroyed. Humans are capable of paying attention to patterns in their own thinking in a similar fashion (see, for example, Chi et al., 1994). Self-watching in Copycat and Metacat The Copycat architecture has been discussed at length elsewhere (Mitchell, 1993; Hofstadter and FARG, 1995), so details will be omitted here. Briefly, the program consists of a long-term mem- ory of concepts about the letter-string world, called the Slipnet, together with a short-term memory for perceptual structures, called the Workspace. In the Workspace, small nondeterministic agents called codelets examine the letters of an analogy problem (“abc ⇒ abd; mrrjjj ⇒ ? ”, for example), and build