*AI - The art and science of making computers do interesting things that
are not in their nature.*

- Introduction
- SlugBot: an autonomous slug-collecting robot that eats its slugs
- Massively parallel computation by chemical diffusion and reaction
- Molniya and the Shuttle in shape space: ballistic intrusion detection and other applications of immunocomputing
- An equivalence between Hopfield neural nets and non-monotonic inference
- Combining neural networks with category theory and colimits
- Style as a choice of blending principles: formalising metaphor by diagrams of semiotic morphisms
- Synaesthesia, Derek tastes of earwax, and the Skeekit-Woogle test

Editing this newsletter is a spare-time occupation, and
interesting papers pile up faster than I can read them.
This would probably
happen even were I not spending time
on anything else. Isaac Asimov explained in
his
essay
*The
Sound of Panting*
that the panting wasn't him chasing pretty
research students around the room, but merely
the sound of
trying to keep up
with the literature. A much more futile task;
and that was in the 1950s.
So the
links below are to papers and topics that I have
only skimmed. But
I think they're interesting, and perhaps even important.

www-robotics.usc.edu/~ikelly/tta.html
—

*SlugBot: Towards True Autonomy*, by
Ian Kelly, 2003:

"Most mobile robots are not truly autonomous; most operate
in simplified environments.
…
On the other hand, even the simplest animals are self-sufficient, both in
terms of information processing and energy. The aim of this project is to
build a robot with animal-like self-sufficiency in both information and
energy. We don't expect to be able to match the speed and performance of a
cheetah chasing a zebra, within the time frame of this project, so we
decided to chase something slightly slower ... slugs. Apart from their
relative ease of capture (compared to zebras), slugs were chosen because
they are a major pest, are reasonably plentiful, have no hard shell or
skeleton, and are reasonably large. It is also more technologically
interesting to catch mobile prey rather than just grazing on plants".

www.ias.uwe.ac.uk/Energy-Autonomy-New/New%20Scientist%20-%20EcoBot%20II.htm
—

*Energy Autonomy: Towards a truly Autonomous Robot*.

Description and videos of EcoBot II, whose
energy comes from bacterial decomposition of dead
flies and rotten fruit in
microbial fuel cells.

www.ias.uwe.ac.uk/Energy-Autonomy-New/New%20Scientist%20-%20Detailed%20EcoBot%20project.htm
—

*Robots and autonomy: The EcoBot project*.

Chemical details of how
the fuel cells derive
electricity from bacterial metabolism.

www.newscientist.com/article.ns?id=dn6366
—

*Self-sustaining killer robot creates a stink*,
by Duncan Graham-Rowe, *New Scientist*,
9th September 2004:

"In its present form, EcoBot II still has to be manually fed fistfuls of
dead bluebottles, but the ultimate aim of the UWE robotics team is to make
the droid predatory, using sewage as a bait to catch the flies.
…
'One of the great things about flies is that you can get them to come to
you,' says Melhuish. The team has yet to tackle this, but speculates that
it would involve using a bottleneck-style flytrap with some form of pump to
suck the flies into the digestion chambers.
…
In tests, EcoBot II travelled for five days on just
eight fat flies - one in each MFC [microbial fuel cell]".

www.cs.york.ac.uk/nature/uc06/images/york-reaction-diffusion-computers-tutorial.pdf
—

*REACTION-DIFFUSION COMPUTERS:
5th International Conference on Unconventional Computation
4-8 September 2006, University of York, UK*.

This
ad for a tutorial on reaction-diffusion computing explains this to be:
"computation with travelling waves in
reaction-diffusion non-linear media. A reaction-diffusion computer is a
massively parallel computing device, where micro-volumes of the chemical
medium act as elementary few-bit processors; and chemical species diffuse
and react in parallel. In the reaction-diffusion computer both the data and
the results of the computation are encoded as concentration profiles of the
reagents, or local disturbances of concentrations, whilst the computation
per se is performed via the spreading and interaction of waves caused by
the local disturbances".

www.cs.york.ac.uk/nature/workshop/papers/Adamatzky.pdf
—

*Reaction-diffusion computers:
Wet, Weird and Wonderful*, by
Andrew Adamatzky and Ben De Lacy Costello.

A two-page summary of reaction-diffusion research.
Potential applications
include
image processing, planning, robot navigation,
and computational geometry; and implementation of
novel logic gates and storage devices.

uncomp.uwe.ac.uk/adamatzky/programmingrd.pdf
—

*Programming Reaction-Diffusion Processors*,
by Andrew Adamatzky, 2005.

Research paper on three ways of programming reaction-diffusion
processors: one computes Voronoi
diagrams, two implement logic gates.
Includes references to previous work.

uncomp.uwe.ac.uk/adamatzky/
—

Adamatzky's home page. Links to
a great number of publications. Frustratingly,
most are not on the Web.

www-static.cc.gatech.edu/~turk/reaction_diffusion/reaction_diffusion.html
—

Greg Turk's page on
creating surface texture in computer graphics by
simulating
reaction-diffusion.

www.cems.uwe.ac.uk/ucg/immuno.htm
—

*RESEARCH IN IMMUNOCOMPUTING:
Dr. Alexander Tarakanov*.

Near the bottom of this page is a fuzzy but tantalising
screen shot showing clusters of blobs, one labelled
"Molniya-1 N67" and the other "Shuttle Ferret". To
quote the text:
"We develop a rigorous mathematical basis of a novel approach to computing,
immunocomputing, and its applications to solve specific real world
problems. Immunocomputing is inspired by the biological principles of
information processing by proteins and immune networks. We introduce new
mathematical abstractions of formal protein and formal immune network. We
developed a rigorous proof that a formal immune network is able to learn,
to recognize, to solve problems and to represent languages based on the
theory of linguistic valence. We present some applied results, such as
computing of ecological atlases, monitoring of most dangerous infections,
detecting critical situations in near Earth space, information security,
etc. These results allow us to speak about the hardware implementation of
the immunocomputing in so-called immunochip. We have developed several
versions of software emulator of the immunochip. Currently we are
developing a biochip (biological microchip) for immunocomputing".

web.auth.gr/chi/PROJECTSIMCOM/T2.doc
—

*INFORMATION SECURITY WITH FORMAL IMMUNE NETWORKS*,
by Alexander Tarakanov.

I wrote about
some applications of artificial immune systems in our
March
issue. Despite curiosity about the
images above, I didn't mention Tarakanov's work, partly
because almost all his papers are on pay-to-view sites.
This one
isn't. It also looks more rigorous than some other
AIS research, and therefore worth following up.

web.auth.gr/chi/PROJECTSIMCOM/TS1.doc
—

*Immunocomputing for Surveillance the Plague in Central Asia*,
by
Alexander Tarakanov and Svetlana Sokolova.

Another of Tarakanov's few freely-available papers, modelling the dynamics
of
epidemic disease in central Asia. The authors conclude that
immunocomputing can
focus attention
on the most dangerous situations, something beyond the
possibilities of traditional statistics.

www.illc.uva.nl/Publications/ResearchReports/PP-2004-16.text.pdf
—

*Nonmonotonic Inferences and Neural Networks*, by
Reinhard Blutner. Marked as to appear in
*Knowledge, Rationality and Action*, Issue 2, 2004.

It's good to see work that tries to
unify separate areas of research.
Blutner
demonstrates an equivalence between
non-monotonic inference, and Hopfield nets considered as
dynamical systems: "The main
results are (a) that
certain activities of connectionist networks can be interpreted as
nonmonotonic inferences, and
(b) that there is a strict correspondence between the coding of knowledge
in Hopfield networks
and the knowledge representation in weight-annotated Poole systems. These
results show the
usefulness of nonmonotonic logic as a descriptive and analytic tool for
analyzing emerging
properties of connectionist networks. Assuming an exponential development
of the weight
function, the present account relates to optimality theory - a general
framework that aims to
integrate insights from symbolism and connectionism. The paper concludes
with some
speculations about extending the present ideas".

citeseer.ist.psu.edu/healy00category.html
—

*Category Theory Applied to Neural Modeling and Graphical
Representations*, by Michael Healy. Minor revision of Paper NN0648 in
*International Joint Conference on Neural Networks* 2000, Como,
Italy.

In programming, functions can be defined in terms of other functions;
lists can have sublists as elements; records can have subrecords
as fields; classes can inherit from subclasses.
The resulting functions, lists, records, or classes can then be further
combined and extended … and so on, as many times as required.
Such recursive
extensibility is rare in neural nets, but appears to be what Healy is
trying to provide. He uses
category theory's "colimit" operation, a kind of
generalised sum. This builds a description of a system from
descriptions of its components —
program modules, database views, processes, or whatever —
given information about how these are interconnected. Importantly,
it works when different components share information, as in
two overkapping database views, or
two program modules both of which share a submodule.
Or, as Healy has, two neural nets that gain different views of a concept
through different sensors.

The categorical model, with functors from a category of concepts to a category of neural network components and natural transformations between these functors, provides a mathematical model for neural structures consistent with concept-subconcept relationships. Colimits of diagrams show how concepts can be combined, and how a concept can be re-used many times in forming more complex concepts. Functors map commutative diagrams to commutative diagrams, capturing this aspect of the colimit structure. Natural transformations express the fusion of single-mode sensor representations of concepts in the same erent implementations of the concept hierarchy at all levels and in a consistent fashion. This mathematical model appears to be compatible with a model of the primate brain proposed by Damasio[1]. We hope that others will find this model interesting and useful for neural network analysis and design.

www.sandia.gov/cog.systems/cognitive_workshop_2004/Cog%20Workshop%202004%20Presentations/Healy,%20Michael.ppt
—

Brief slide presentation on
*Category Theory and Cognitive Neural Systems: A
Mathematical Semantic Model*,
by Michael Healy and Thomas Caudell, 2004.
Note the "association area" between two nets in the diagram.

www.cs.ucsd.edu/~goguen/pps/style04.pdf
—

*Style as a Choice of Blending Principles*, by
Joseph Goguen and D. Fox Harrell.

This paper applies category theory too, to
formalise
how the meaning of a metaphor is derived from its
components. The components are the meanings of words
such as "house" and "boat"; the result is an optimal
blend of these, as in the word "houseboat". In
a
Cognitive
Linguistics List
announcement, Goguen says
this work offers:

- a new approach to style based on blending;
- a structural blending generalization of conceptual blending;
- a mathematical formalization of blending;
- an implementation approach to syntax based on 2, 3 and cognitive grammar;
- a reconsideration and broadening of optimality principles; and
- a poetry generation system based on all the above.

www.cs.ucsd.edu/users/goguen/papers/blend.html
—

*Formal Notation for Conceptual Blending*, by
Joseph Goguen.

"This note concerns formal notation for conceptual spaces,
conceptual
morphisms, and conceptual blending, in the sense developed in
*An
Introduction to Algebraic Semiotics, with
Applications to User Interface Design*, …
Here, we expand
and correct certain aspects of the
discussion there; also, instead of the notation developed there, we use the
notation of the
OBJ algebraic
specification language, which is executable, though we use that
capability
mainly to check the syntax of our theories and morphisms, i.e., as a type
checker for algebraic semiotics. For much more detail on OBJ, see the
manual,
*Introducing
OBJ*.

www.bbc.co.uk/sn/tvradio/programmes/horizon/derek_prog_summary.shtml
—

*Derek Tastes of Earwax*, from the BBC
*Science & Nature: TV & Radio Follow-up*
for *Horizon*.

"Imagine if every time you saw someone called Derek you got a strong taste
of earwax in your mouth. It happens to James Wannerton, who runs a pub.
Derek is one of his regulars. Another regular's name gives him the taste
of wet nappies. For some puzzling reason, James's sense of sound and taste
are intermingled". The page, which links to tests anyone can do, and
some questions and answers about synaesthesia,
continues with evidence
suggesting that we are all
synaesthetic to some extent.

www.bbc.co.uk/radio4/reith2003/lecture4.shtml
—

*Lecture 4: Purple Numbers and Sharp Cheese*,
from the 2003 Reith Lectures by Vilayanur Ramachandran.

Ramachandran desscribes synaesthesia in some detail, and
explains the kiki-booba test. This demonstrates
one form of "synaesthesia" — of cross-modal neural
association — that almost all of us have.

www.analogsf.com/0602/behindthestory2.shtml
—

*The Science Behind the Story:
The Skeekit-Woogle Test*, by
Carl Frederick, concerning his story
about synaesthesia in
*Analog*. Same test as Ramachandran's; different name.

www.sdc.org.uk/general/features/feature_music.htm
—

*Hearing colours at the symphonie fantastique*, by
Julian Asher, in *The Colourist*, Summer 2003.

An account of coloured-music synaesthesia and
of the first large-scale study on it, made possible
by CDs.

www.users.bigpond.com/tstex/synaesthesia.htm
—

*Synaesthesia - A Cognitive Model of Cross Modal Associationm*
by
Andrew Lyons,
Composition Unit,
Sydney Conservatorium of Music,
University of Sydney.

A short paper with drawings of the "photisms"
seen by coloured-hearing synaesthetes. These
apparently resemble "Kluver's form constants",
relatively simple shapes also seen in
hallucinations and
"primitive" art.

www.apa.org/monitor/mar01/synesthesia.html
—

*Everyday fantasia: The world of synesthesia*,
by Siri Carpenter, *Monitor on Psychology*,
Volume 32, Number 3, March 2001.

Popular-science
account of some imaging studies and proposed neural
mechanisms. The latter are not yet detailed:
psychologist Peter Grossenbacher from Naropa University is
quoted as saying "The trouble with theorizing in this area is that
we're underconstrained by data. There isn't the right kind of data, yet,
to differentiate between these different theories".

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