Message-ID: <084355Z19041995@anon.penet.fi>
Path: cantaloupe.srv.cs.cmu.edu!rochester!udel!gatech!howland.reston.ans.net!Germany.EU.net!EU.net!news.eunet.fi!anon.penet.fi
Newsgroups: comp.ai.edu
From: an232240@anon.penet.fi
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Organization: Anonymous contact service
Reply-To: an232240@anon.penet.fi
Date: Wed, 19 Apr 1995 08:40:05 UTC
Subject: "S+E=constant"
Lines: 59



PLEASE MAIL RESPONSES TO MY E-MAIL ADDRESS 

From ,
 Samiullah S.M.
 <sam@wipinfo.soft.net>



						" S+E=constant"	
						------------------------------

Abstract :
  The complexity of problem solving in a system is given as a sum 
  of two factors .
  

Science and art comprise the two extremes of the problem-system spectrum .
Science  , in plain terms , consists of domains , each domain has a set of 
 facts , rules , states and usually an alphabet which aids in describing the 
 states .In essence , there is an attempt to be algorithmic ( or mechanical)
 in approach .The problem goal is usually to change the state of the system 
-which is done using an algorithm (which in turn manipulates the facts and
 rules in the system) -to the goal state .

Art , on the other hand  , could be motivated by
  complex homomorphisms .
In essence , the approach is non-algorithmic.(elaborated later in the article ).
Often the process that produced an algorithm is non-algorithmic .
Here , the state of the system is changed  to the goal state , 
 due to the solver's "feel " for the manipulation of facts and rules of the 
 system.

From the viewpoint of the problem system
we now state that 
					S + E = K ( constant for a problem system )
where S is the skill factor or artfulness or compile time effort and E is 
the mechanical effort or runtime effort used by the solver.
The S can also be called the non-determinism in the system and E the 
determinism in the system.
The constant is a measure of the magnitude of the problem , so, when n
 solvers are given a problem , their S , E mixtures are likely to be different,
 so , each can take a different time to solve . Different problems have 
different K since the magnitude of each problem can be different.

We can see an example to illustrate, 
When the author studied an algorithms course , it was found that for a given
problem , the algorithms got better as one went into the text .The algorithms
obviously got cleverer ( they improved in time and space complexity ),though
the problem remained the same .We can see that as the algorithms got cleverer
the S factor increased and their E factor decreased so that the sum total 
always remained the same .

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