AI Expert Newsletter
AI - The art and science of making computers do interesting
things that are not in their nature.
June 2006
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".
Past newsletters are available at either www.ddj.com
or www.ainewsletter.com.
As ever, interesting links and ideas for future issues are very
welcome.
Until next month,
Jocelyn <popx@j-paine.org>
For questions about the www.ainewsletter.com
site, contact Dennis
Merritt
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