Re: Humans think fuzzy?

From: Harris Georgiou (xgeorgio@hol.gr)
Date: Thu May 17 2001 - 15:28:18 MET DST

  • Next message: Russ Heersberger: "Re: Humans think fuzzy?"

    Ο Marcello Savarese <marcello.savarese@tin.it> έγραψε στο μήνυμα συζήτησης:
    asUJ6.1030$2i2.24525@news1.tin.it...
    > I read with attention you response about this subject....
    > I'm agree with you considerations on argument, but I think that the
    > imprecision is the natural aspect of fuzzy logic .
    > I think that the uncertainty is linked whit probability concept.
    > If I say that an object ( eg. apple) is 0.75( or 75%) an apple I expose a
    > uncertainty but if I say this obj belongs 0.8 to apple membership, I
    > means an imprecision.
    > So in the probability topic I say that this obj is or isn't an apple
     with
    > grade of probability( in ex: 75%)) ( uncertainty), in the Fuzzy topic I
    say
    > that this obj is a "wrong apple" but is an apple( imprecision).
    > I hope that I explained my concept in the better manner.....
    > I love to say that probability is a distance measure from true and the
    fuzzy
    > is a distance measure from real.
    >
    > Where my reasoning is wrong?
    > What do you think about it?
    >

    Hello,
    The following reply might be useful:

    > Hi everyone,
    >
    > A neuron clearly showing the fuzzy logic INCLUSIVE OR property of passing
    the
    > maximum value among it inputs has been found in
    > the auditory system of the frog brain (see the home page of my web site
    > http://neurocomputing.org for the data and reference). Yet neural signals
    have
    > more than one parameter (dimension) unlike the values used in conventional
    logic
    > and probability. Neural signals have a pulse width (number of action
    potentials)
    > and within that pulse the action potentials have a frequency. In addition
    the
    > frequency has a variance and often a decay. Finally, a variable latency
    often
    > occurs before the pulse is triggered.
    >

    All true. Even more, R.Penrose suggests a rather quantum neural model for
    the brain cells which takes into account quantum effects in signal
    propagation, pointing to a theory as complex as (maybe) hyperstrings for
    physics. However, this does not mean that current implementations a
    deprecated (Newton's laws still stand, even as a sub-set, but quite real and
    descriptive). With neural nets, we do not want to construct an exact
    formulation for the biological neurons, but simply to simulate their basic
    fuctionality. Neural networks seem to have accomplished that in many cases,
    whether low-level signal processing or high-level logic functions. The
    problem is not whether the NN can learn to act as a logic unit, but rather
    how AI can help that this unit has the congitive knowledge of what it does.

    > Certainly neural signals have a validity value in addition to a truth
    value to
    > make it at least a 2-dimensional multivalued logic (a generalization of
    fuzzy and
    > modal logic as pointed out by Stephen Lehmke in a posting to this
    newsgroup). But
    > how are these 2 dimensions used and defined? How do they interact? Is
    there a
    > relation to probability? And what about their variances?

    The vector space of a NN can actually contain many thousands of dimensions,
    this does not mean that we should try and translate them all into
    multi-dimensional logic. NN try to fit an adaptive system into a set of
    pre-determined constraints (not necessarily logic ones), not describe the
    cognitive attributes of the environment which is implied by them (NN is not
    AI in terms of inference logic).

    > Probability and logic and not independent. Consider this question about a
    car
    > belonging to the sample set of vehicles. "What is the probability that a
    car is
    > behind a wall?" The probability depends upon the precision of the
    definition for
    > "car". Does "car" include a pick-up truck, a sport utility vehicle, a van?

    I can not agree with that. Probability has nothing to do with fuzziness.
    Propability applies to mutually exclusive states, while fuzziness applies
    when things belong to more than one state at the same time. The proposition
    "What is the probability that a car is behind a wall?" is clearly predicate
    logic (with the addition of probability calculations). The same question in
    terms of fuzziness should be "How much of the car is behind the wall?" - it
    may sound strange but it stands quite well (with value from 0 to 1), as long
    as we forget our perception that a car can not actually be in front and
    behind the wall (two-valued logic => mutually exclusive states =>
    propability calculations).

    ___________________________
    Harris Georgiou
    Informatics Systems Analyst (MSc)
    mailto:xgeorgio@hol.gr

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