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AIList Digest Volume 5 Issue 186
AIList Digest Wednesday, 22 Jul 1987 Volume 5 : Issue 186
Today's Topics:
Queries - AI Application for DBase of New Chemical Substances &
Software Reuse,
Science Fiction - Immortality via Computer,
Techniques - Garbage Collection Suppression,
Philosophy - Natural Kinds,
Courses - Philosophy Courses on Artificial Intelligence &
Logic and Computability, AI and Formal Learning Theory
----------------------------------------------------------------------
Date: 20 Jul 1987 1220-EDT
From: Holger Sommer <SOMMER@C.CS.CMU.EDU>
Subject: AI/Expert system application for DBase of New Chemical Substances
I am involved in an EPA/NIOSH sponsered project with the title :
"Engineering and Toxic Characterisation Studies and Development of Unit
Operations Predictive Models for New Chemicals"
This project intends to develop an intelligent database and predictive
models to help EPA in the evaluation of premanufacture notices.
The research is directed and conducted to establish data base model for
use in predicting workplace releases and exposures resulting from
manufacturing , processing , use or disposal of new chemical substances.
The main activities of this project are :
* Formulation of conceptual data base models for filtration and drying
unit operation
* Assessment and characterization of worker exposure in manufacturing
plants and pilot plants
* Incorporation of sampling data and other relevant information into
the data base framework
* Development and validation of computerized predictive models for
assessment of workplace releases and exposures.
What we try to accomplish in this project is to automate the evaluation
process for premanufacture notices and provide a systematic data base to
assist in this evaluation.
My questions to the AI-list audience are :
1) Are there projects underway with similar content ( not particular
related to chemicals but other domains ) ?
2) We need information about existing data base programs which
interface with predictive models. We are looking for a flexible
programming tool to accomplish the above described assignments.
Thank you for any information I will receive through this network.
Please send responses to : H.T. Sommer .... Sommer@c.cs.cmu.edu
------------------------------
Date: 18 Jul 87 15:17:05 GMT
From: cbmvax!vu-vlsi!ge-mc3i!sterritt@RUTGERS.EDU (Chris Sterritt)
Subject: Re: Software Reuse (short title)
Hello,
I've been following the discussion of this avidly, but am new to the
programming languages (?) ML, SML, and LML. Could someone (ideally mail
me directly so as not to clog the net!) send me information on these langauges,
so that I might find out more?
Along the ideas of the discussion, if I remember my Computability
theory correctly -- doesn't it make some sense that to show an algorithm
(either computable or to prove it) you need to give an almost algorithmic
description, as in an inductive proof? So isn't this what Lisp is (I'm a lisp
hacker at work). I'd think that Church's Lambda Calculus would shed some light
on this discussion, as I believe that that was what he was trying to do with
the calculus. Generally, I agree that to specify an algorithm IN ENOUGH DETAIL,
you will probably wind up writing at least as much information down as the code
itself. I think that 'Requirements' as we define them in 'Software Engineering'
presume a *lot* of human intelligence.
Any comments?
Chris Sterritt
------------------------------
Date: 20 Jul 87 13:21:39 GMT
From: sunybcs!rapaport@RUTGERS.EDU (William J. Rapaport)
Reply-to: sunybcs!rapaport@RUTGERS.EDU (William J. Rapaport)
Subject: Re: Immortality via Computer
In article <8707200504.AA05729@ucbvax.Berkeley.EDU> MNORTON@rca.COM writes:
>
>Concerning the AP story on attaining immortality via computers, readers
>of AIList intrested in thinking more about this may wish to read ...
... or Justin Leiber's _Beyond Rejection_. Leiber is a philosopher and
also the son of SF writer Fritz Leiber. The novel is about a society in
which brain tapes are made and installed in new bodies; the minds tend
to reject the bodies.
------------------------------
Date: Mon, 20 Jul 87 21:43:00 EDT
From: Chester@UDEL.EDU
Subject: Re: Garbage Collection Suppression
The direct way to avoid garbage collection in lisp is to define your own `cons'
function that prefers to get cell pairs from an `available list', calling the
regular `cons' only when the `available list' is empty. A `reclaim' function
that puts cell pairs on the `available list' (using `rplacd') will be needed
also. See any book on data structures. The technique can be used for cell
pairs and gensym atoms, if needed, but in my experience, not with strings or
numbers. String manipulations can usually be avoided, but a program that
crunches a lot of numbers cannot avoid consuming memory and eventually
triggering garbage collection (at least in VAX lisp). I wish there were some
way for a user to reclaim numbers so that they could be reused as cell pairs
can. If so, I could write all my lisp programs so that they don't need to
garbage collect. It would also be nice to have a built-in `reclaim' function
that would work in conjunction with the built-in `cons'; it would be dangerous
for novices, but handy for the experienced.
By the way, recursion in itself doesn't cause garbage collection; VAX lisp is
smart enough to reclaim the memory used for the function stack automatically.
Daniel Chester
chester@dewey.udel.edu
------------------------------
Date: 21 Jul 87 17:05:53 GMT
From: rlw@philabs.philips.com (Richard Wexelblat)
Reply-to: rlw@philabs.philips.com (Richard Wexelblat)
Subject: Re: Natural Kinds
It is amusing and instructive to study and speculate on children's language
and conceptualization. (Wow! That construct's almost Swiftean!) For those
who would read further in this domain, I recommend:
Brown, Roger
A First Language -- The Early Stages
Harvard Univ. Press, 1973
MacNamara, John
Names for Things -- A Study of Human Learning
MIT Press, 1984
------------------------------
Date: 21 Jul 87 16:56:08 GMT
From: rlw@philabs.philips.com (Richard Wexelblat)
Reply-to: rlw@briar.philips.com (Richard Wexelblat)
Subject: Re: Natural Kinds
In article <8707161942.AA13065@nrl-css.ARPA> mclean@NRL-CSS.ARPA
(John McLean) writes:
>However, I think the issue being raised about recognizing penguins,
>chairs, etc. goes back to Wittgenstein's _Philosophical_Investigations_:
Actually, the particular section chosen is a bit too terse. Here is more
context:
Consider, for example the proceedings that we call `games.' I mean board-
games, card-games, ball-games, Olympic games, and so on. What is common to
them all?--Don't say: ``There must be something common, or they would not be
called `games' ''--but look and see whether there is anything common to all.
--For if you look at them you will not see something that is common to all,
but similarities, relationships, and a whole series of them at that ... a
complicated network of similarities overlapping and criss-crossing; sometimes
overall similarities, sometimes similarities of detail.
I can think of no better expression to characterize these similarities
than ``family resemblances''; for the various resemblances between the
members of a family: build, features, colour of eyes, gait, temperament,
etc. etc. overlap and criss-cross in the same way.--And I shall say: `games'
form a family.
* * *
This sort of argument came up in a project on conceptual design tools a few
years ago in attempting to answer the question: ``What is a design and how
do you know when you have one?'' We attempted to answer the question and got
into the question of subjective classifications of architecture. What is a
``ranch'' or ``colonial'' house? If you can get a definition that will
satisfy a homebuyer, you are in the wrong business.
* * *
Gratis, here are two amusing epigrams from W's Notebooks, 1914-1916:
There can never be surprises in logic.
~~~~~
One of the most difficult of the philosopher's tasks is to
find out where the shoe pinches.
------------------------------
Date: 17 Jul 1987 1505-EDT
From: Clark Glymour <GLYMOUR@C.CS.CMU.EDU>
Subject: Philosophy Courses on Artificial Intelligence
SEMINAR IN LOGIC AND COMPUTABILITY:
ARTIFICIAL INTELLIGENCE AND FORMAL LEARNING THEORY
- Offered by: Department of Philosophy, Carnegie-Mellon University
- Instructor: Kevin T. Kelly
- Graduate Listing: 80-812
- Undergraduate Listing: 80-510
- Place: Baker Hall 131-A
- Time: Wednesday, 1:30 to 4:30 (but probably not the full period).
- Intended Audience: Graduate students and sophisticated undergraduates
interested in inductive methods, the philosophy of science,
mathematical logic, statistics, computer science, artificial
intelligence, and cognitive science.
- Prerequisites: A good working knowledge of mathematical logic and
computation theory.
- Course Focus: Convergent realism is the philosophical thesis that the
point of inquiry is to converge (in some sense) to the truth (or to
something like it). Formal learning theory is a growing body of
precise results concerning the possible circumstances under which
this ideal is attainable. The basic idea was developed by Hilary
Putnam in the early 1960's, and was extended to questions in
theoretical linguistics by E. Mark Gold. The main text of the
seminar will be Osherson and Weinstein's recent book Systems that
Learn. But we will also examine more recent efforts by Osherson,
Weinstein, Glymour and Kelly to apply the theory to the inductive
inference of theories expressed in logical languages. From this
general standpoint, we will move to more detailed projects such as
the recent results of Valiant, Pitt, and Kearns on polynomial
learnabilitly. Finally, we will examine the extent to which formal
learning theory can assist in the demonstrable improvement of
learning systems published in the A.I. machine learning literature.
There is ample opportunity to break new ground here. Thesis topics
abound.
- Course Format: Several introductory lectures, Seminar reports, and a
novel research project.
PROBABILITY AND ARTIFICIAL INTELLIGENCE
- Offered by: Department of Philosophy, Carnegie-Mellon University
- Instructor: Kevin T. Kelly
- Graduate Course Number: 80-312
- Undergraduate Course Number: 80-811
- Place: Porter Hall, 126-B
- Time: Tuesday, Thursday, 3:00-4:20
- Intended Audience: Graduate students and sophisticated undergraduates
interested in inductive methods, the philosophy of science,
mathematical logic, statistics, computer science, artificial
intelligence, and cognitive science.
- Prerequisites: Familiarity with mathematical logic, computation, and
probability theory
- Course Focus: There are several ways in which the combined system of
a rational agent and its environment can be stochastic. The agent's
hypotheses may make claims about probabilities, the agent's
environment may be stochastic, and the agent itself may be
stochastic, in any combination. In this course, we will examine
efforts to study computational agents in each of these situations.
The aim will be to assess particular computational proposals from the
point of view of logic and probability theory. Example topics are
Bayesian systems, Dempster-Shafer theory, medical expert systems,
computationally tractable learnability, automated linear causal
modelling, and Osherson and Weinstein's results concerning
limitations on effective Bayesians.
- Course Format: The grade will be based on frequent exercises and
possibly a final project. There will be no examinations if the class
keeps up with the material.
------------------------------
Date: 17 Jul 87 16:54:45 EDT
From: Terina.Jett@b.gp.cs.cmu.edu
Subject: Seminar - Logic and Computability, AI and Formal Learning
Theory
SEMINAR IN LOGIC AND COMPUTABILITY
ARTIFICIAL INTELLIGENCE AND FORMAL LEARNING THEORY
Offered by: Department of Philosophy
Instructor: Kevin T. Kelly
Grad Listing: 80-510
Undergrad Listing: 80-510
Place: Baker Hall 131-A
Time: Wed, 1:30 - 4:30
Intended Audience: Graduate students and sophisticated undergraduates
interested in inductive methods, the philosophy of science, mathematical
logic, statistics, computer science, artificial intelligence, and cogni-
tive science.
Prerequisites: A good working knowledge of mathematical logic and comp-
utation theory.
Course Focus: Convergent realism is the philosophickal thesis that the
point of inquiry is to converge (in some sense) to the truth (or to
something like it). Formal learning theory is a growing body of precise
results concerning the possible circumstances under which this ideal is
attainable. The basic idea was developed by Hilary Putnam in the early
1960's, and was extended to questions in theoretical linguistics by E.
Mark Gold. The main text fo the seminar will be Osherson and Weinstein's
recent book Systems That Learn. But we will also examine more recent
efforts by Osherson, Weinstein, Glymour and Kelly to apply the theory to
the inductive inference of theories expressed in logical languages. From
this general standpoint, we will move to more detailed projects such as
the recent results of Valiant, Pitt, and Kearns on polynomials learn-
abilitly. Finally, we will examine the extent to which formal learning
theory can assist in the demonstrable improvement of learning systems
published in the A.I. machine learning literature. There is ample
opportunity to break new ground here. Thesis topics abound.
Course Format: Serveral introductory lectures, Seminar reports, and
a novel research project.
------------------------------
End of AIList Digest
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