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Previous
talks:
| Date |
Speaker |
Title |
| 8/4/2009 |
Navneet Bhalla, University of Calgary |
Designing
Self-assembling Systems |
| Self-assembly and
developmental
processes go hand-in-hand with design, through evolution by means of
natural selection, to create the plethora of complex biological systems
we see throughout nature. However, designing and creating artificial
self-assembling systems continues to be extremely challenging. One
aspect that remains an open problem is how to design a set of
components and their environmental conditions such that the set of
components self-assemble into a desired entity. Techniques have been
developed for a few specific applications, but a general approach has
not been achieved. The focus of my research is on the design aspects of
self-assembly. I will present key results of my research over the past
five years: from the creation of a fish-like structure from simple
mechanical components of varying form, to the creation of a virtual
coffee mug from generic spherical components, and finally to the
creation of simple mechanical structures resulting from components
following a rule-based approach. These results form the basis to the
bottom-up design process I am currently developing, for which I will
provide an overview. As well, I will present preliminary research on
the incorporation of rapid prototyping to aid in designing
self-assembling systems. |
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| Date |
Speaker |
Title |
| 16/2/2006 |
Dr
Daniel Polani, University of Hertfordshire |
Organization
of Information Flows in the Perception-Action Loop of Agents |
Information
is an essential and omnipresent resource and has long been suspected as
a major factor shaping the emergence of intelligence in animals and as
a guideline to construct artificial intelligent systems. In search for
fundamental principles guiding the self-organization of neural
networks, Linsker (1988) formulated a number of information-theoretic
hypotheses. His model (and most of its successors) was purely passive.
However, recent work by Touchette and Lloyd (2000) extending early work
by Ashby (1953), as well as some work by Polani et al. (2001) has shown
that actions can be incorporated into the information- theoretical
analysis.
As was found by Klyubin et al. (2004), incorporating actions into an
information-theoretic formalization of the perception action-loop of
agents has dramatic consequences in terms of self-organization
capabilities of the processing system. As opposed to Linsker's model
which required some significant pre-structuring of its neural network,
this new model makes only minimal assumptions about the information
processing architecture. The agent's "embodiment", i.e. the coupling of
its sensors and actuators to the environment, is sufficient to give
rise to structured pattern detectors driven by optimization principles
applied to the information flow in the system.
In the present talk, we will motivate Shannon information as a primary
resource of information processing, introduce a model which allows to
consider agents purely in terms of information and show how this model
gives rise to the aforementioned observations. If there is time, the
talk will discuss the use of information-theoretic methods to structure
the information processing also in real robot systems. |
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| Date |
Speaker |
Title |
| 6/10/2005 |
Prof.
Risto Miikkulainen, University of Texas at Austin |
Solving
sequential decision tasks with Neuroevolution |
| Intelligent
agents in the real world often have to make several decisions before
they get feedback on how well they are doing: for example, operating a
vehicle, playing a game, and routing packets in a network are examples
of such sequential decision tasks. Decision policies for such tasks are
difficult to design by hand, but it is often possible to learn them
through interaction with the environment. Neuroevolution, where neural
networks are evolved with genetic algorithms, is a new and powerful
method for learning such policies. In this talk, I will review recent
advances in neuroevolution methods, and present several applications
ranging from rocket control and autonomous vehicles to robotics and
interactive video games.
Risto
Miikkulainen is a Professor of Computer Sciences at the University of
Texas at Austin. He received an M.S. in Engineering from the Helsinki
University of Technology, Finland, and a Ph.D. in Computer Science from
UCLA. His current research includes models of natural language
processing, self-organization of the visual cortex, and evolving neural
networks with genetic algorithms. Professor Miikkulainen is an author
of over 180 articles in these research areas, and the books
"Computational Maps in the Visual Cortex" (Springer, 2005), "Lateral
Interactions in the Cortex: Structure and Function", and "Subsymbolic
Natural Language Processing". He is an editor of the Machine Learning
Journal and Journal of Cognitive Systems Research.
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| Date |
Speaker |
Title |
| 25/7/2005 |
Dr.
Steve DiPaola, Simon Fraser University |
Research
in
intelligent character, creature and artistic interactive systems |
| Steve
DiPaola will discuss and demonstrate his research and the research of
the iVizLab (ivizlab.sfu.ca/research) that he directs, which uses an
interdisciplinary approach to create human centered interactive,
creative and communication systems. Best known for his expertise in 3D
facial animation and communication systems, DiPaola will demonstrate
iFace which uses an intelligent behavior based parameterized approach
to synthetic facial communication. He will also show the lab's ongoing
work with the Vancouver Aquarium to create an Artificial Life based
(digitalbiology.com), Virtual Beluga Whale Interactive where visitors
can collaboratively interact with a simulated pod of wild beluga
whales. He will also discuss his work with creative AI systems, that
browse face-space for the Maxis game 'The Sims', create moving
paintings by extracting emotion out of music and more recently use an
automatic creative fitness function to evolve abstract portrait painter
programs using Cartesian Genetic Programming (dipaola.org/evolve). |
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| Date |
Speaker |
Title |
| 10/5/2005 |
Prof.
Danny van Noort, Seoul National University |
DNA
Computing |
Information
processing can be seen in various aspects of our life. Actually, nature
can be seen an entity where information is processed. DNA is a good
example; it is the template of life. This talk will focus on a more
basic level where it will be shown how DNA molecules can be used to do
computations.
Microflow reactors are an important tool for realizing computing with
DNA, RNA or proteins. The advantages are, for example, the need for
very small volumes of biological solutions and increased reaction
rates. Furthermore, they provide control over the flow of solutions,
i.e. the flow of information. Microflow reactors can be made from
problem specific to re-configurable systems. A designed DNA strand
contains bit information in the sequence of its nucleotides. Extraction
of single-stranded DNA molecules, from a sequence space {Si} (the
DNA-library), can be made by using complementary capture probes (CP), a
short-single DNA strands. Hybridisation between these two is a
selection, i.e. a YES or NO.
However, on cellular level it is also possible to do computations.
Think of metabolic pathways as a series of logical switches triggered
by inhibitors and promoters. This can be seen as a computational
network as well. Furthermore, RNA can be used to program cells for
desired outputs, like phage. This talk will guide one through various
aspects of computation in microreactors anda little inbiology.
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| Date |
Speaker |
Title |
| 28/1/2005 |
Dr.
Jonathan Mackenzie, University of Cambridge |
Evolving
models of morphogenesis in plants |
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| Date |
Speaker |
Title |
| 26/8/2004 |
Dr.
Tim Hutton, UCL |
Making
a
Self-Reproducing Cell in a Two-Dimensional Artificial Chemistry |
Nobody knows
how to make an artificial life system that undergoes the evolutionary
growth of complexity. This is a shame because if we had a working model
we could learn a lot about life and evolution through experiment.
An artificial chemistry is a simplification of real chemistry, designed
to be much cheaper to simulate on computer.
In the talk I will show how it is possible to make a self-reproducing
cell in an artificial chemistry by surrounding a replicating molecule
with a semi-permeable membrane. The molecule can carry an arbitrary
amount of information, encoded in a material form as a sequence of
bases, as in DNA. The cells produce enzymes through a decoding of their
base sequence, and these enzymes trigger reactions essential to the
cell's survival.
The cells reliably reproduce over many generations under environmental
pressure for resources. By creating cells in a material-based
artificial chemistry we hope that the system might have the potential
for open-ended, creative evolution. |
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| Date |
Speaker |
Title |
| 15/7/2004 |
Prof.
Nik Kasabov, Auckland University of Technology |
Evolving
Connectionist Systems and Applications in Bioinformatics and Brain Study |
| This
presentationgives the background theory of the Evolving Connectionist
Systems (ECOS) along with their new development and applications in
Bioinformatics and Brain study. ECOS are neural network models that
evolve their structure and functionality through learning from data in
an on-line and off-line incremental modes, in both supervised and
unsupervised modes, and facilitate rule extraction and rule
manipulation. The evolving process of an ECOS is defined by parameters,
“genes”. ECOS extend further the classical
knowledge-based neural networks.
Simple ECOS use parameters that have vague analogues with genes in the
biological sense. These systems have been applied to challenging
problems in Bioinformatics, such as: DNA sequence analysis, microarray
gene expression profiling of cancer, finding gene regulatory networks
(GRN), medical prognostic systems [1]. Recently new, biologically
plausible, ECOS have been developed as computational neurogenetic
models, where the ECOS parameters correspond to real genes expressed in
the brain and related to learning processes, or to brain diseases. A
dynamic model of a GRN within each neuron is evolved during the
modeling process that governs the neuronal processes. All neurons have
a spiking behavior to form spiking neural networks, characterized by a
spectral profile [3]. This model is applied on problems of brain
modeling that include modeling learning processes and epilepsy. Further
directions for biologically plausible ECOS include: dynamic, evolving
neurogenetic models; modeling other brain processes and diseases;
general applications of ECOS as universal classification and prognostic
systems; self-replicating ECOS; hardware implementation. |
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| Date |
Speaker |
Title |
| 6/5/2004 |
Dr.
Robert Smith, University of the West of England |
Agents,
evolution and the end of chaos |
The role of
“AI” is shifting from dreams of automata that
generate optimized results to all manner of problems, to the reality of
computers and human beings in creative, collaborative relationships
within everyday organizations. This talk begins by considering
agent-based models of these relationships. It introduces a view of
agents that combines the 19th century views of Peirce with the most
recent work of Kauffman. The talk then introduces the novel view that
all manner of multi-agent systems can be seen as evolutionary in
character; with birth, death, mutation, and the exchange of gene-like
and meme-like information between entities. The view will show how the
lessons of evolutionary computation can be interpreted and utilized
within the context of complex, multi-agent systems. Given this view,
the talk will proceed to consider how lessons from complex adaptive
systems theory may create control laws for such systems in the future.
Final comments consider how such observations may influence the
creative collaborations of humans and computers in the future.
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