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Theory of Generalised Problem-Solving
As engineers
we are often always called to tricky & sticky situations to resolve
& we are expected to resolve them completely
thats why the social sciences people label us as problem-solvers
pple problems
we solve
supposedly & completely
situations can be:
- Tricky:
there are 2 aspects obvious & hidden aspects
the obvious aspects are that can be immediately or almost immediately
observed & detected
the causes derived
the remedies must address both the problems & causes of those problems
the hidden aspects are the tricky parts
they are like time-bombs that await the ignorant engineer who thinks he has solved the obvious portions
which might be master-minded by the hidden aspects
- Sticky:
there are limits to the resources tools, labour, expertise, time, etc.
that can be used
some problems are just too extensive, long-term & haphazard
sometimes here, other times there
there seems no apparent patterns or behaviour & no models & fundamental understanding to the phenomenon
these problems stick like mud
& the ignorant engineer who keeps the same techniques & methodologies against such problems
would find no progress in solution
due to resource constraints
Hence, we need to generalise our engineering approach towards tricky & sticky problems such that it can applied with confidence
independent of the problem domain
. Basically, this involves:
- Problem awareness:
fundamental understanding of the problem
qualitative aspect: be able to conceptualise, simplify & reform into familiar & confident analogous problems
quantitative aspect: able to observe, detect & even model to support the qualitative aspect
- Methodology:
understand that e world system
as e problem system
is a dynamic & flexible one
in that
it can be altered & modified in such ways that can maximise the following
(Remedy effect) / (Constraints) ratio
the method must:
- Amplify remedy effect:
in our case
stabilise controlled structure
then, magnify active control efficiency & performance
- Reduce constraints or restraints:
reduce complexities, unknowns
by simplifications, verifications & re-configuring systems
Take the example of experiment set-up:
- What is the problem? Any hidden aspects?
- What are the resources (structure components, connections, sensors, controller, actuators & time left)? Have we utilised them all & to the maximum in the correct way?
- Have we amplified the control effects?
- Have we reduced the restraints that are confining our control effects?
Theory of Generalised Problem-Solving:
- Criteria: set a set of criteria or what we want to achieve by doing all these
- Problem understanding:
awareness of how to reach the criteria
- Selected solution:
methodology to increase ratio of (remedy effects)/(constraints) to achieve criteria
Spirited Research Spirit