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AIList Digest Volume 6 Issue 010
AIList Digest Friday, 15 Jan 1988 Volume 6 : Issue 10
Today's Topics:
Queries: Table-Tennis-Playing Robot & M. Selfridge &
TRC Users & Graphical Representation of Rule Base,
Philosophy - Evolution of Intelligence & Empirical Science,
Neuronal Systems - MLNS Public-Domain Simulator Tool Set Effort
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Date: 14 Jan 88 00:32:47 GMT
From: dlfe91!hucka@umix.cc.umich.edu (Michael Hucka)
Subject: query: table-tennis-playing robot?
Within the last half-year I read an article which described a successful
robotic device capable of playing table-tennis. Unfortunately I can't remember
where I came across it. Has anyone else read about this or know where I can
get more information about it? I am interested in learning about the research
and technical issues the system's creators had to address.
Mike
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Computer Aided Engineering Network, University of Michigan, Ann Arbor MI 48109
ARPA: hucka@caen.engin.umich.edu
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Date: 14 Jan 88 20:04:39 GMT
From: ece-csc!ncrcae!gollum!rolandi@mcnc.org (rolandi)
Subject: M. Selfridge
Does anyone know the email (or other mail) address of M. Selfridge of:
Selfridge, M. 1980. A Process Model of Language Acquisition. Ph.D.
diss., Technical Report, 172, Dept of Computer Science, Yale
University.
?
Thanks.
walter rolandi
rolandi@gollum.UUCP ()
NCR Advanced Systems, Columbia, SC
u.s.carolina dept. of psychology and linguistics
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Date: 12 Jan 88 02:41:49 GMT
From: linc.cis.upenn.edu!levy@super.upenn.edu (Joshua Levy)
Subject: Looking for TRC users
I'm interested in how many people are using TRC, and
what they are using it for. If you use TRC, plan to,
or are just interested in it, please send me email.
(Especially if you have modified or improved it in any way.)
Thanks.
TRC: Translate Rules to C, is a program which takes an OPS
like rule language and compiles it into C code. It is a PD
program.
Joshua Levy
levy@linc.cis.upenn.edu
------------------------------
Date: 14 Jan 88 12:11:57 GMT
From: mcvax!hermanl@uunet.uu.net (Herman Lenferink)
Subject: graphical representation of rule base
I am searching for algorithms / approaches to represent a (production)
rule base in the form of a graph / tree.
The premise of a rule can have several conditions, connected with
AND / OR connectives. A rule may also have more than one conclusion
(using an AND connective).
However, I am also interested in suggestions for representations of other
rule formats.
Any hints, literature references, or even source code are VERY welcome.
If there is interest, I will summarize the responces.
Thanks in advance,
Herman Lenferink
CWI, Amsterdam, Netherlands
hermanl@piring.cwi.nl
------------------------------
Date: 14 Jan 88 07:30:59 GMT
From: well!wcalvin@lll-crg.llnl.gov (William Calvin)
Reply-to: well!wcalvin@lll-crg.llnl.gov (William Calvin)
Subject: Re: Evolution of Intelligence
My favorite short definition is that of Horace B. Barlow in 1983:
"Intelligence... is the capacity to guess right by
discovering new order."
There are some related quotes at p.187 of my book THE RIVER THAT FLOWS UPHILL.
William H. Calvin
University of Washington NJ-15, Seattle WA 98195
wcalvin@well.uucp 206/328-1192
------------------------------
Date: Tue, 5 Jan 88 11:50 EST
From: Bruce E. Nevin <bnevin@cch.bbn.com>
Subject: empirical science of language
[Excerpted from the NL-KR Digest. Bruce mentions fundamental
difficulties in the study of lingistics and psychology. Are
there similar viewpoints on AI? Roger Schank mentions at least
one in his recent AI Magazine article: If AI is the study of
uniquely human capabilities, then any algorithm derived from AI
negates its own domain. -- KIL]
The status of linguistics as a science has been a vexed question for a
very long time. There are a number of good reasons. Probably the
central one is this: in all other sciences and in mathematics, you can
rely on the shared understanding of natural language to provide a
metalanguage for your specialized notations and argumentation. In
linguistics you cannot without begging fundamental questions that define
the field. There is an exactly parallel difficulty in psychology: a
psychological model must account for the investigator on the same terms
as it accounts for the object of investigation. The carefully crafted
suspension of subjectivity that is so crucial to experimental method
becomes unattainable when subjectivity itself is the subject. (See
Winograd's recent work, e.g. _Understanding Computers and Cognition_ for
reasons why computer modelling of natural language is not possible, on
the usual construal of what computer modelling is. I have references to
work that gets around this "Framer Problem" if you are interested.)
[...]
------------------------------
Date: Sun, 10 Jan 88 22:05:37 EST
From: weidlich@ludwig.scc.com (Bob Weidlich)
Subject: MLNS Announcement
A PROPOSAL TO THE NEURAL NETWORK RESEARCH COMMUNITY
TO BUILD A
MULTI-MODELED LAYERED NEURAL NETWORK SIMULATOR TOOL SET (MLNS)
Robert Weidlich
Contel Federal Systems
January 11, 1988
The technology of neural networks is in its infancy. Like all other major new
technologies at that stage, the development of neural networks is slowed by
many impediments along the road to realizing its potential to solve many sig-
nificant real world problems. A common assumption of those on the periphery
of neural network research is that the major factor holding back progress is
the lack of hardware architectures designed specifically to implement neural
networks. But those of us who use neural networks on a day to day basis real-
ize that a much more immediate problem is the lack of sufficiently powerful
neural network models. The pace of progress in the technology will be deter-
mined by the evolution of existing models such as Back Propagation, Hopfield,
and ART, as well as the development of completely new models.
But there is yet another significant problem that inhibits the evolution of
those models: lack of powerful-yet-easy-to-use, standardized, reasonably-
priced toolsets. We spend months of time building our own computer simula-
tors, or we spend a lot of money on the meager offerings of the marketplace;
in either case we find we spend more time building implementations of the
models than applying those models to our applications. And those who lack
sophisticated computer programming skills are cut out altogether.
I propose to the neural network research community that we initiate an
endeavor to build a suite of neural network simulation tools for the public
domain. The team will hopefully be composed of a cross-section of industry,
academic institutions, and government, and will use computer networks, pri-
marily Arpanet, as its communications medium. The tool set, hereinafter
referred to as the MLNS, will ultimately implement all of the significant
neural network models, and run on a broad range of computers.
These are the basic goals of this endeavor.
1. Facilitate the growth and evolution of neural network technology by
building a set of powerful yet simple to use neural network simula-
tion tools for the research community.
2. Promote standardization in neural network tools.
3. Open up neural network technology to those with limited computer
expertise by providing powerful tools with sophisticated graphical
user interfaces. Open up neural network technology to those with
limited budgets.
4. Since we expect neural network models to evolve rapidly, update the
tools to keep up with that evolution.
This announcement is a condensation of a couple of papers I have written
describing this proposed effort. I describe how to get copies of those docu-
ments and get involved in the project, at the end of this announcement.
The MLNS tool will be distinctive in that will incorporate a layered approach
to its architecture, thus allowing several levels of abstraction. In a sense,
it is a really a suite of neural net tools, one operating atop the other,
rather than a single tool. The upper layers enable users to build sophisti-
cated applications of neural networks which provide simple user interfaces,
and hide much of the complexity of the tool from the user.
This tool will implement as many significant neural network models (i.e., Back
Propagation, Hopfield, ART, etc.) as is feasible to build. The first release
will probably cover only 3 or 4 of the more popular models. We will take an
iterative approach to building the tool and we will make extensive use of
rapid prototyping.
I am asking for volunteers to help build the tool. We will rely on computer
networks, primarily Arpanet and those networks with gateways on Arpanet, to
provide our communications utility. We will need a variety of skills - pro-
grammers (much of it will be written in C), neural network "experts", and
reviewers. Please do not be reluctant to help out just because you feel
you're not quite experienced enough; my major motivation for initiating this
project is to round-out my own neural networking experience. We also need
potential users who feel they have a pretty good feel for what is necessary
and desirable in a good neural network tool set.
The tool set will be 100% public domain; it will not be the property of, or
copyrighted by my company (Contel Federal Systems) or any other organization,
except for a possible future non-commercial organization that we may want to
set up to support the tool set.
If you are interested in getting involved as a designer, an advisor, a poten-
tial user, or if you're just curious about what's going on, the next step is
to download the files in which I describe this project in detail. You can do
this by ftp file transfer and an anonymous user. To do that, take the follow-
ing steps:
1. Set up an ftp session with my host:
"ftp ludwig.scc.com"
(Note: this is an arpanet address. If you are
on a network other than arpanet with a gateway
to arpanet, you may need a modified address
specification. Consult your local comm network
guru if you need help.)
[Note: FTP generally does not work across gateways. -- KIL]
2. Login with the user name "anonymous"
3. Use the password "guest"
4. Download the pertinent files:
"get READ.ME" (the current status of the files)
"get mlns_spec.doc (the specification for the MLNS)
"get mlns_prop.doc (the long version of the proposal)
If for any reason you cannot download the files, then call or write me the
following address:
Robert Weidlich
Mail Stop P/530
Contel Federal Systems
12015 Lee Jackson Highway
Fairfax, Virginia 22033
(703) 359-7585 (or) (703) 359-7847
(leave a message if I am not available)
ARPA: weidlich@ludwig.scc.com
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End of AIList Digest
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