hudak@siemens.UUCP (Michael J. Hudak) (01/07/88)
SEMINAR ANNOUNCEMENT
Speaker: Peter Cariani
Systems Science Dept., Thomas J. Watson School of Engineering
State University of New York at Binghamton
Title: Structural Preconditions for Open-Ended Learning
through Machine Evolution
Location: Siemens Corporate Research & Support, Inc.
3rd floor Multi-Purpose Room
Princeton Forrestal Center
105 College Road East
Princeton, NJ 08540-6668
Date: Thursday, 14 January 1988
Time: 10:00 am (refreshments: 9:45)
For more information call Mike Hudak: 609/734-3373
Abstract
One of the basic problems confronting artificial life simulations is
the apparent open-ended nature of structural evolution, classically known
as the problem of emergence. Were it possible to construct devices with
open-ended behaviors and capabilities, fundamentally new learning tech-
nologies would become possible. At present, none of our devices or models
are open-ended, due to the nature of their design and construction.
The best devices we have, in the form of trainable machines, neural net
simulations, Boltzmann machines and Holland-type adaptive machines,
exhibit learning within the categories fixed by their feature spaces.
Learning occurs through the performance dependent optimization of alter-
native I/O functions. Within the adaptive machine paradigm of these
devices, the measuring devices, feature spaces, and hence the real world
semantics of such devices are stable. Such machines cannot create new
primitive categories; they will not expand their feature and behavior
spaces.
Over phylogenetic time spans, however, organisms have evolved new sensors
and effectors, allowing them to perceive more and more aspects of their
environments and to act in more and more ways upon those environments.
This involves a whole new level of learning: the learning of new primitive
cognitive and behavioral categories. In terms of constructible devices,
this level of learning encompasses machines which construct and select
their own sensors and effectors, based upon their real world performance.
The semantics of the feature and behavior spaces of such devices thus
changes so as to optimize their effectiveness as categories of perception
and action. Such devices construct their own primitive categories, their
own primitive concepts. Evolutionary devices could be combined with
adaptive ones to both optimize primitive categories and I/O mappings
within those categories.
Evolutionary machines cannot be constructed through computations alone.
New primitive category construction necessitates that new physical
measuring structures and controls come into being. While the behavior of
such devices can be represented to a limited degree by formal models,
those models cannot themselves create new categories vis-a-vis the real
world, and hence are insufficient as category-creating devices in their
own right. Computations must be augmented by the physical construction
of new sensors and effectors implementing processes of measurement and
control respectively. This construction process must be inheritable and
replicable, hence encodable into symbolic form, yet involving the autono-
mous, unencoded dynamics of the matter itself.
The paradigmatic example of a natural construction process is protein
folding. A one-dimensional string of nucleotides, itself a discrete,
rate-independent symbolic structure, is transformed into continuous, rate
dependent dynamics having biological function through the action of the
physical properties inherent in the protein chain itself. The functional
properties of speed, specificity, and reliability of action are thus
achieved with symbolic constraints but without the explicit direction of
rules.