There have been a number of attempts to define a methodology for
the analysis of complex dynamic biological systems. One of these
attempts is schema theory which lays down the conceptual framework
for knowledge representation inspired from biological and cognitive
studies. Schema theory (Arbib 1992) contributes to Distributed Artificial
Intelligence (DAI), and relies on cognitive science and brain theory.
Its applications are in areas such as robotics, where cognitive,
sensory, and motor processes can be represented at appropriate levels
of detail. Schemas provide a level of representation at a "neuropsychological"
level intermediate between gross neural task descriptions and detailed
There have beed developed a range of schema theory based models,
ranging from biological systems, such as models addressing lesion
data on the toad's prey-acquisition and predator avoidance systems
(Cobas and Arbib 1990; 1991; Arbib and Lee 1993; Corbacho and Arbib
1995), to models based on artificial neural networks, such as sensorimotor
integration of robotic systems (Fagg et al. 1992).
A number of simulation systems have been developed to tackle different
aspects of schema theory. In particular, ASL
- Abstract Schema Language, is currently being developed to
provide a modeling platform for neural based schema systems.
Adaptation and Learning
The area of learning and adaptation in dynamic systems has been
addressed by many scientists in the traditional area of artificial
intelligence, with systems such as SOAR, and in neural network systems,
with biologically inspired learning such as hebbian , or artificial
learning such as with back-propagation. Learning in traditional
artificial intelligence systems is explicit, through pre-established
high level mechanisms, while learning in neural modeling is implicit,
generally specified as a mechanism at the level of individual synaptic
weights. In traditional neural networks, the system has no way of
knowing or reasoning about its state, as opposed to the more traditional
explanation-based reasoning, thus constraining any optimizations
or analysis which may be done to higher levels of cognition. We
are yet to see evolved models where cognitive learning, (i.e., learning
at a high level), is causally connected to learning at lower levels.
Adaptation and Learning at multiple
levels is one of the research goals in the laboratory.
Reflective Meta-Level Architectures
Reflective meta-level architectures add "introspective"
capabilities to computational systems by providing dynamic adaptability
to internal and external processing constraints [Smith 1984]. Reflection
in object-oriented systems [Maes 1987] separates base-objects from
their corresponding meta-object views, such as operational, resource,
statistical, and migrational, where modification to any meta-object
would cause a corresponding base-object modification, and similarly
the other way around [Okamura et al. 1992]. Benefits of "exposing"
system implementation in order to provide greater expressiveness
at a more abstract level [Kiczales 1992] are seen in areas such
as parallel systems, where greater efficiency in parallel compilers
can be achieved by "opening" both the compiler and run-time
system [Lamping et al. 1992]. Similarly, in the area of neural networks,
a reflective architecture design can provide more adequate control
of modular neural networks [Smieja and Muhlenbein, 1992], as well
as improve the computational efficiency of the network [Boers and
In order to manage the inherent complexity in the development and
simulation of computational models at multiple levels of granularity,
we are currently developing a reflective meta-level architecture
for NSL (Neural Simulation Language) and ASL (Abstract Schema Language),
to provide the following mechanisms:
1. Provide processing (load balancing) and communication monitor
and control capabilities in distributed NSL/ASL architecture.
2. Provide tracking capabilities for different ASL/NSL schema and
neural network model and data versions.
3. Provide real time monitor and control capabilities when linking
to real robots.