Abstract
The traditional approach to complex problems in science
and engineering is to break down each problem into a set of
primitive building blocks, which are then combined by rules
to form structures. In turn, these structures can be taken
apart systematically to recover the original building blocks
that went into them. Connectionist models of such complex
problems (especially in the realm of cognitive science) have
often been criticized for their putative failure to support this
sort of compositionality, systematicity, and recoverability of
components. In this paper we discuss a connectionist model,
Recursive Auto-Associative Memory (RAAM), designed to
deal with these issues. Specifically, we show how an initial approach to RAAM involving arbitrary building-block
representations placed severe constraints on the scalability
of the model. We describe a re-analysis the building-block
and “rule” components of the model as merely two aspects
of a single underlying nonlinear dynamical system, allowing
the model to represent an unbounded number of well-formed
compositional structures. We conclude by speculating about
the insight that such a “unified” view might contribute to our
attempts to understand and model rule-governed, compositional behavior in a variety of AI domains.