The Bottom-Up Symbolic (BUS) approach to AI

Humans have the ability to abstract a "high-level" symbolic
representation (concepts) of the dynamics in their surrounding
world. This seems to be the key to the impressive cognitive abilities
they display. Concepts (eg. such as "chair") allow humans to make
intelligent decisions about how to act or what to expect even if they
have never experienced the particular chair currently in their
presence.

Historically, AI has evolved into two distinct approaches for
installing such concepts into AI agents. First, the Top-Down approach
of Logic and Machine Learning. Here, symbols representing concepts are
installed in an agent by the system designer. The criticism then is
that, even though the symbols mean something to the designer, they
do not mean anything to the agent itself, since they are not grounded
in its sensory and actuatory experience of the environment (see
Harnard 1990 for example).

The other approach historically taken to AI is the Bottom-up approach
of neural networks (connectionism). The aim here is to achieve high-level
cognition by connecting simple neuronal (ie non-symbolic) elements into
networks. It was hoped that a symbolic (conceptual) representation of
the environment would emerge out of these networks, however it has not
occurred to date. We call this the BUNS (Bottom Up Non-Symbolic)
approach to AI due to the non-symbolic nature of neurons.

A third possible approach to AI is to grow concepts in a bottom up
fashion but not restrict the substrate to be non-symbolic (ie
neuronal). We call this the Bottom Up Symbolic (BUS) approach. A small
body of work using this method exists in the literature
(eg. Rosenstein and Cohen AAAI 99 and Pierce and Kuipers AI 92, plus others - see my
paper).  These works do not include real world and random agent
exploration in their concept formation process, while we do (see
further discussion in my paper). The result is that the concept
formation process in our work requires a statistical basis, which is
what distinguishes it from the others.