Abstract
As autonomous robots become more complex in their behavior, more sophisticated software
architectures are required to support the ever more sophisticated robotics software. These
software architectures must support complex behaviors involving adaptation and learning,
implemented, in particular, by neural networks. We present in this paper a neural based schema
[2] software architecture for the development and execution of autonomous robots in both
simulated and real worlds. This architecture has been developed in the context of adaptive
robotic agents, ecological robots [6], cooperating and competing with each other in adapting to
their environment. The architecture is the result of integrating a number of development and
execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and
MissionLab, a schema-based mission-oriented simulation and robot system. This work
contributes to modeling in Brain Theory (BT) and Cognitive Psychology, with applications in
Distributed Artificial Intelligence (DAI), Autonomous Agents and Robotics.
Areas: Robotics, Agent-oriented programming, Neural Nets
Keywords: Autonomous Robots, Autonomous Agents, Schemas, Neural Networks,
Architecture
1 Introduction
To enable the development and execution of complex behaviors in autonomous robots
involving adaptation and learning, sophisticated software architectures are required.
The neural schema architecture provides such a system, supporting the development
and execution of complex behaviors, or schemas [3][2], in a hierarchical and layered
fashion [9] integrating with neural network processing.
In general, schema theory helps define brain functionality in terms of concurrent
activity of interacting behavioral units called schemas. Schema-based modeling may
be specified purely on behavioral data (ethology), while becoming part of a neural
based approach to adaptive behavior when constrained by data provided by, e.g., the
effects of brain lesions upon animal behavior (neuroethology). Schema modeling
provides a framework for modeling at the purely behavioral level, at the neural
network level or even below [28]. In terms of neural networks, neural schema theory
provides a functional/structural decomposition, in strong contrast with models which
employ learning rules to train a single, otherwise undifferentiated, neural network to
respond as specified by some training set. Neural schema-based modeling proceeds
at two levels: (1) model behavior in terms of schemas, interacting functional units;
(2) implementation of schemas as neural networks based on neuroanatomical and
neurophysiological studies. What makes the linking of structure and function so
challenging is that, in general, a functional analysis proceeding "top-down" from
some overall behavior need not map directly into a "bottom up" analysis proceeding
upwards from the neural circuitry.
The work described in this paper is the product of a collaborative research depicted in
Figure 1.


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