2 research outputs found
A global workspace framework for combined reasoning
Artificial Intelligence research has produced
many effective techniques for solving a wide range
of problems. Practitioners tend to concentrate their efforts in one particular problem solving
paradigm and, in the main, AI research describes new methods for solving particular types of
problems or improvements in existing approaches. By contrast, much less research has considered
how to fruitfully combine different problem solving techniques. Numerous studies have
demonstrated how a combination of reasoning approaches can improve the effectiveness of one of
those methods. Others have demonstrated how, by using several different reasoning techniques,
a system or method can be developed to accomplish a novel task, that none of the individual
techniques could perform. Combined reasoning systems, i.e., systems which apply disparate
reasoning techniques in concert, can be more than the sum of their parts. In addition, they
gain leverage from advances in the individual methods they encompass. However, the benefits
of combined reasoning systems are not easily accessible, and systems have been hand-crafted
to very specific tasks in certain domains. This approach means those systems often suffer from
a lack of clarity of design and are inflexible to extension. In order for the field of combined reasoning
to advance, we need to determine best practice and identify effective general approaches.
By developing useful frameworks, we can empower researchers to explore the potential of combined
reasoning, and AI in general. We present here a framework for developing combined
reasoning systems, based upon Baars’ Global Workspace Theory. The architecture describes a
collection of processes, embodying individual reasoning techniques, which communicate via a
global workspace. We present, also, a software toolkit which allows users to implement systems
according to the framework. We describe how, despite the restrictions of the framework, we
have used it to create systems to perform a number of combined reasoning tasks. As well
as being as effective as previous implementations, the simplicity of the underlying framework
means they are structured in a straightforward and comprehensible manner. It also makes the
systems easy to extend to new capabilities, which we demonstrate in a number of case studies.
Furthermore, the framework and toolkit we describe allow developers to harness the parallel
nature of the underlying theory by enabling them to readily convert their implementations into
distributed systems. We have experimented with the framework in a number of application domains
and, through these applications, we have contributed to constraint satisfaction problem
solving and automated theory formation