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From: AI.CAUSEY@R20.UTEXAS.EDU (Robert L. Causey)
Newsgroups: comp.ai.digest
Subject: Natural Kinds
Message-ID: <12319416814.40.AI.CAUSEY@R20.UTEXAS.EDU>
Date: Sat, 18-Jul-87 16:17:43 EDT
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Posted: Sat Jul 18 16:17:43 1987
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In a message posted 7/15, John McCarthy says that philosophers
have recently introduced the concept of natural kind, and he
suggests how this concept may be useful in AI.  I think this
deserves serious comment, both historical and substantive.  The
following is lengthy, but it may illustrate some general
characteristics about the relationships between philosophy and AI. 

                         HISTORY
In their messages, Ken Laws and others are correct -- the idea of
natural kinds is not new.  It is at least implicit in some
Pre-Socratic Greek philosophy, and Aristotle extensively
developed the idea and applied it in both philosophy and biology. 
Aristotle's conception is too "essentialist" to fit what McCarthy
refers to. 

In the late 1600's John Locke developed an impressive empiricist
analysis of natural kinds.  Further developments were contributed
in the 1800's in J.  S.  Mill's, _A_System_Of_Logic_.  Mill also
made important contributions to our understanding of inductive
reasoning and scientific explanation; these are related to
natural kinds. 

In our century a number of concepts of natural kinds have been
proposed, ranging from strongly empiricist "cluster" approaches
(which need NOT preclude expanding the cluster of attributes
through the discovery of new knowledge, cf.  McCarthy 7/17), to
various modal analyses, to some intermediate approaches.  Any of
these analyses may have some value depending on the intended
application, but the traditional notion of natural kinds has
almost always been connected somehow with the idea of natural
laws. 

                    SUBSTANTIVE ISSUES
1.  Whatever one's favorite analysis might be, it is important to
distinguish between a NATURAL kind (e.g., the compound silicon
dioxide, with nomologically determined physical and chemical
attributes), and a functional concept like "chair".  There is
generally not a simple one-to-one correspondence between our
functional classifications of objects and the classification
systems that are developed in the natural sciences.  This is true
in spite of the fact that we can learn to recognize sand,
penguins, and chairs.  But things are not always so simple -
Suppose that Rip van Winkle learns in 1940 to recognize at sight
a 1940-style adding machine; he then sleeps for 47 years.  Upon
waking in 1987 he probably would not recognize at sight what a
thin, wallet calculator is.  Functional classifications are
useful, but we should not assume that they are generated and
processed in the same ways as natural classifications.  In
particular, since functional classifications often involve an
abstract understanding of complex behavioral dispositions, they
are particularly hard to learn once one gets beyond simple things
like chairs and tables. 

2.  Even discovering the classic examples of NATURAL kinds (like the
classification of the chemical elements) can be a long and
difficult process.  It requires numerous inductive
generalizations to confirm that the attributes in a certain Set
of attributes each apply to gold, and that the attributes in some
other Set of attributes apply to iodine, etc.  We further
recognize that our KNOWLEDGE of what are the elements of these
Sets of attributes grows with the general growth of our
scientific knowledge.  Also, we need not always use the same set
of attributes for IDENTIFICATION of instances of a natural kind. 
Most of this goes back to Locke, and philosophers have long
recognized the connection between induction and classification;
Carnap, Hempel, Goodman, and others, have sharpened some of the
issues during the last 50 years. 

3.  Now, getting back to McCarthy's suggestion -- in his second
message (7/17) he writes: "...for a child to presume a natural
kind on hearing a word or seeing an object is advantageous, and
it will also be advantageous to built (sic) AI systems with this
presumption." His 7/15 message says, "When an object is named,
the system should generate a gensym, e.g., GOO137.  To this
symbol should be attached the name and what the system is to
remember about the instance." This is an interesting suggestion,
but it prompts some comments and questions:

i) Assuming that children do begin to presume natural kinds at
some stage of development, what inductive processes are they
using, what biologically determined constraints are affecting
these processes, and what prior acquired knowledge is directing
their inductions.  These are interesting psychological questions. 
But, depending on our applications, we may not even want to build
robots that emulate young children.  We can attach a name
to a gensym, but it is not at all easy to decide "...what the
system is to remember about the instance,"  or to specify how
it is to process all of the stuff it generates in this manner.

ii) Children receive much corrective feedback from other people;
how much feedback will we be willing or able to give to the
"maturing" robots? Will the more mature robots help train the
naive ones?

iii) Given that classification does involve complex inductive
reasoning, we need to learn a lot more about how to implement
effective inductive procedures, where "induction" is understood
very broadly. 

iv) If the AI systems (robots, etc.) are to learn, and reason with,
functional concepts, then things get even more complex.  Ability
to make abstractions and perform complex analogical reasoning
will be required.  In my judgment, we (humans) still have a lot
to learn just about the representation of functional knowledge. 
If my Rip van Winkle story seems farfetched, here is a true
story.  I know a person who is familiar with the appearance and
use of 5 1/4 inch floppy diskettes.  Upon first seeing a 3.5 inch
mini-diskette, she had no idea what it was until its function was
described.  Knowledge of diskettes can extend to tracks, sectors,
etc.  The concept of natural kinds is relatively simple (though
often difficult to apply); functional concepts and their
relations with physical structures are harder subjects.
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