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{\large \bf Topics continuing from previous meetings}
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 The reason behind my interest to develop a `general' configuration
was my disenchantment with the AI course I was taking at that time.
The AI techniques exposed to me showed no motivation to develop 
commonsense as we know it and where addressed, talked about common
sense being fed into the system as a knowledge base. Further, the
emphasis on completeness rather than on relevance and context 
made the techniques look like offshoots of logic and symbolic
problem solving. Having come to the conclusion that common sense
and reasonable competence is a more relevant goal, I proceeded to
reason that this can be be more easily achieved by a robot with
the sensors to accept data in the form of senses including speech;
classify, rationalize and incorporate it into its programming. The
goal then would shift to developing a suit of sensors, data(knowledge)
representation, sureness factors  and local conflict resolution. 
This would also need consolidation issues like propagation of conclusions
(and consequences), backtracks and classification
without completeness, and still maintain a (regionally) consistent system.
This is my first attempt at crystallizing this on paper and would like to 
weed out possible premature specifics. The sensor arrays and sound
would be convoluted (over space and time) to detect motion, shape
and other qualitative features and give a quantitative output whose
interpretation would be learnt by feedback. I am interested in knowing
if this has been attempted and discarded or shown to be unsound, since
it would prevent me from expending the effort to think this over in the
next few years. \\



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