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From: briand@infmx.UUCP (brian donat)
Newsgroups: comp.ai
Subject: Re: Adaptive vs. intelligent (was Re: "Intelligence")
Summary: Higher Levels of Adaptiveness
Message-ID: <1597@infmx.UUCP>
Date: 20 Jun 89 23:12:20 GMT
References: <1533@stl.stc.co.uk>
Organization: Informix Software Inc., Menlo Park, CA.
Lines: 135


> David Riches

>I belonged to an Alvey project called Adaptive Intelligent Dialogues
>in which we investigated Adaptation.  During the course of this 4 year
>project we produced a taxonomy of adaptive systems.  This is
>reproduced below.  The levels refer to the 'level of adaptivity' with
>corresponding meanings in the computer world and w.r.t. evolution.

>---------------------------------------------------------------------
>Levels	Computers	Evolution	Features

>0.5	Tailorable/	-		Deferred selection
>	Adaptable

OK, so here we have a system which (by the term tailorable), is assumed
to solve problems based upon what functionaly is a rigid switch, for a
decision maker.  Each activation of the switch gives a defined output, 
whether the switch is activated by a single or multiple inputs.

>1	Adaptive	Tropism/	Apparent Learning
>			reflexes	(i.e. fully determined by design)
>					Discrimination

By the term 'Apparent Learning' it is assumed that it realy doesn't fit
the definition although it appears to.    So perhaps we have here a 
system which is only slightly more evolved than the rudimentary switch.
In such a system, it might be assumed that it alters its composition so
that as it experiences an event, it remembers the event by storing something
or by altering something so that the next time the equivalent event occurs,
its response is 'tuned' and it need not waste time analyzing and just does
the appropriate thing.

FINE.  OK.    Again it appears that the programmer controls outcomes
although this time, the inputs and sequencing of responses need not be
rigid.   Inputs (environment) begin to influence the system's character.


>2	Self-Regulating	Operant		Learning; Varied responses
>			conditioning	selected for different situations;
>					Evaluation by trial and error

Another evolution?    What could this be?     Real learning?   

I'm sorry, but I'm lost here.   This seems to be something similar to
level 1, except that you're saying, that it actually 'tries' multiple
inputs sequentially and selects one for subsequent processing based 
upon some programmer defined criteria.

I don't see learning here.   Learning would mean that the system 
auto-generates new code (it's own response) to go beyond the 
programmer's built in restrictions, tests the new code and then, 
judges which code handled the input situation better and keeps it for
subsequent 'reference'.   I say reference here, because this leaves
the door open for the system to reject implementation of the same response,
if causal values should weigh it's decison to do so.   Learning then 
becomes the machine's confidence in the same solution.   

I believe what we have so far, is three examples of tailored adaptation
with varying complexities.


>3	Self-Mediating	Internal	Planning, Problem Solving;
>			evaluation	rule-mediated representation;
>					Initial evaluation internal to
>					system


What is planning without the ability to recognize a problem?   What is
a problem to a system which can not 'generate' problems?   When you 
say 'problem solving', each of your earlier levels seem to have as
much to do with this same type of problem solving as this level.    
The system is solving your problem, ultimately, based on your rules!

The problem is given by you.  You expect it to give a solution which you
have already defined.   This system is no less 'programmer rigid' than 
those you site in the first two levels of adaptability.

Intelligence must be autonomous to the point that it observes less formal
rules than 'tailored rules'.   If you really had a system which was
at an adaptive level such that it could 'plan' and 'solve problems' based
on planning, you'd have a system that should be able to 'define and solve
its OWN problems'. 

Perhaps my perspective is wrong, but it really does seem like you're 
just building a more complex machine which is really still operating on
the same adaptive principles (limitation to programmer definition) which
you have already defined in level 0.5 as 'tailored'.

>4	Self-Modifying	Abstraction	Evaluating the evaluation;
>					Generalisation; Meta-knowledge

This doesn't help much.   Looking back on results of programmer defined
tests is just another conditional in your switch which amounts to another
programmer defined test and thus is also tailored.


Perhaps these suffice as 'primitives' for a single adaptive level
which is defined by variations which are tailored.


Your level 2 'primitives' might better allow introduction of a device for
recognizing the 'need' for a response such that the recognition of the 
need and the response to it are both auto-generated, unless the response
is already conditioned; in other words, two part problem resolution where
problems are auto-defined as a response in themselves and actions are
not only taken from a known selection of givens, but are created as 
required and the givens become mallable over time.

The machine should create it's own problems and resolutions and it should 
do this for all situations which first threaten it's survival, prevent 
it's own self-demise and for those situations whereby it enhances it's 
own survival and then, for general and logical resolutions.


If you evolve an adaptive system which can guarantee its own survival from
its own recognition of problems and its own resolutions to those problems,
you'll have obtained a level two adaptive system.


Level 3 would inlcude problem solving of the type which goes beyond 
mere survival and defines and resolves problems which are in the realm 
which are common to we humans alone (in terms of life on this planet).


Everything must start somewhere, but I believe your aim's a bit low.


-- brian

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| Brian L. Donat		Informix Software, Inc.  Menlo Park, CA        |
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