Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

16.10 Conclusions and Future Directions
The chapter presented a simple mental model of cognition and demonstrated
its scope of application in intelligent systems capable of reasoning, learning,
co-ordination and control. The integrated view of designing complex systems
like co-ordination of eye-hand-ear of a robot for assembly-line applications
has been illustrated in the chapter in detail.
Much emphasis has been given in the chapter for the development of the
behavioral states of cognition and the analysis of their dynamism with fuzzy
network models. The condition for stability of the system states for the
models has been derived and the estimated range of system parameters for
attaining stability has been verified through computer simulation for
illustrative problems.
The fuzzy networks used in the model of cognition have a distributed
architecture and thus possess a massive power of concurrent computing and
fault tolerance [4]. It also supports pipelining [4] of rules, when used for
reasoning in knowledge-based systems. Further, its functional capability of
modeling neural behavior made it appropriate for applications in both
reasoning and learning systems. The methodology for building an intelligent
system of the above two types by FPN, to the best of the author's knowledge,
is the first work of its kind in Artificial Intelligence. The added advantage of
timed Petri nets in modeling co-ordination problems further extends the scope
of the proposed network in designing complex systems.
It may be noted that besides FPN, many other formalisms could also be
used to model cognition. For instance, Zhang et al. used negative-positiveneutral
(NPN) logic for reasoning [13-16] and co-ordination among distributed
co-operative agents [16] in a cognitive map. However, their model cannot be
utilized for handling as many states of cognition as an FPN can. Kosko’s
model [6] for cognitive map, however, may be considered as a special case of
the FPN-based model presented here.
Realization of the proposed systems in practical form is in progress. For
example, Konar and Mandal [4] designed an Expert System for criminal
investigation with the proposed models. Their system contains approximately
200 rules and an inference engine realized with FPN. It was implemented in
Pascal and tested with simulated criminology problems. Much work, however,
remains for field testing of such systems. The learning, co-ordination and
control models of cognition have yet to be implemented for practical systems.
The use of the proposed models in an integrated system like coordination of
the eye, hand and ear of a robot is an open problem for future research and
development.