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From: david@ztivax.UUCP
Newsgroups: net.arch
Subject: Re: Computational ability of houseflies
Message-ID: <2900009@ztivax.UUCP>
Date: Mon, 5-May-86 08:15:00 EDT
Article-I.D.: ztivax.2900009
Posted: Mon May  5 08:15:00 1986
Date-Received: Thu, 8-May-86 07:42:44 EDT
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Nf-From: ztivax!david    May  5 13:15:00 1986

>...Think about how people
>do arithmetic operations, for example... they do it by table lookup!  

>Recent research seems to suggest that in general a lot of human
>"computation" also works this way, with the interesting enhancement that,
>if you think of it in terms of a table, table entries tend to "attract"
>nearby guesses, so that from an approximation you get pulled into the
>memorized answer. (Likewise, if you make an initial guess that is nearer
>to another (wrong) answer, you may get pulled to that one instead and have
>trouble finding the right answer as a result.) 

I was wondering:  How do I detect errors in thinking?  By seeing by
what other paths the same conclusion can be reached, and seeing if these
"conditions" are also "true".  

Now, lets say we can implement a state machine (software) which can do
these table look ups (perhaps the table is associative to enable
"guesses").  By remembering the state of the local processing (assuming
parallel processing), it should be possible to check the result while
letting the "reasoning" carry on.  If a fault is detected, the
reasoning which has subsequently occurred MIGHT be able to be pulled
back, but certainly not always and not too reliably (side effects would
be difficult).

This seems to be similar to how people reason.  Side effects are often
difficult to eradicate, even if the basis which originally started the
line of reasoning is later found to be false.

Also, this models the way the brain has no central PC, and how
processing on different fronts proceeds as long as new "inferences"
are drawn, and "reasonable" concepts are coelesced into conclusions.

Limiting things to something like current software technology, and to
an organism like a fly which has a known finite set of responses (does
not "create"), lets say the state machine is described using a
optimizable grammar, and built using some kind of hyper-yacc, which
collapses states which are equivalent, and keeps information with the
states which points back at the read/push/reduce tables so all the
"reasons" for reaching this state can be seen if only the state is
known.

On input of stimuli, state transitions occur.  On every state
transition, a new process is spawned to perform a reasonableness
check, if multiple transitions could have caused this state.  If the
reasonableness check fails, then the process group of the
reasonableness check gets killed (all the subsequent processing and
reasonableness checks).  Now, probing around spatially close states
may find a state for which the reasonableness checks will succeed, and
the state is then changed, and processing continues from this point.

But how are states arranged spatially in a nice way?  Guessing does
not work, because the flies will not survive long enough to "evolve"
the correct spatial orientation of states.  In humans, (as was
mentioned in the article I am responding to), "attributes" are used,
although they may be obscure.  Any ideas?

	- David
seismo!unido!ztivax!david