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Report on MASCOS One-Day Symposium
Multi-Agent Systems and Machine Learning
The University of Queensland
Friday 26th November 2004
General
The fields of probability and statistics, computer science
and
information technology are becoming increasingly intertwined. A major
driving force is the fast growing development and application of new
probabilistic and information theoretical approaches to solve complex
problems in a wide rage of areas. Applications can be found
everywhere: in information technology, applied probability and
statistics, engineering, biotechnology, management science,
computational science, financial mathematics, economics, physics,
machine learning and artificial intelligence.
This workshop, sponsored by the ARC Centre of Excellence
for Mathematics and Statistics of Complex Systems (MASCOS)
and the University of Queensland
School of Physical Sciences,
brought together researchers and students
working in the general area of Multi-Agent systems.
There were 45 participants, from Academia, Government and Industry.
Invited speakers
- Andre Costa (MASCOS, University of Melbourne)
- Kelly Fleetwood (University of Queensland)
- Michael Gagen (IMB, University of Queensland)
- Marcus Gallagher (University of Queensland)
- Jonathan Keith (University of Queensland)
- Dirk Kroese (University of Queensland)
- Alex Smola (NICTA, ANU)
- Thomas Taimre (MASCOS, University of Queensland)
[There were no contributed papers]
Venue
Room 141, Priestley Building (Building 67),
St Lucia Campus, University of Queensland
Organizers
Dirk Kroese (University of Queensland)
and Phil Pollett (MASCOS, University of Queensland)
Programme
|
08:45 |
-Registration- |
|
09:00 |
Andre Costa |
Exploration, robustness and optimality of network
routing algorithms which employ "ant-like" mobile agents |
|
10:00 |
Kelly Fleetwood |
An introduction to differential evolution |
|
10:30 |
-Break- |
[Refeshments provided] |
|
10:45 |
Michael Gagen |
Non-myopic multi-player optimization with application
to the iterated prisoner's dilemma |
|
11:15 |
Dirk Kroese |
The Cross-Entropy Method and mathematical programming |
|
12:15 |
-Lunch Break- |
[Lunch provided (barbeque)] |
|
13:30 |
Alex Smola |
Exponential families in feature space |
|
14:30 |
Jon Keith |
Sequence alignment by rare event simulation |
|
15:00 |
-Break- |
[Refeshments provided] |
|
15:15 |
Marcus Gallagher |
Explicit modelling in metaheuristic optimization |
|
15:45 |
Thomas Taimre |
Application of the Cross-Entropy Method to clustering
and vector quantization |
|
16:15 |
-Close- |
Abstracts
- Andre Costa
Exploration, robustness and optimality of network routing
algorithms which employ "ant-like" mobile agents
Abstract:
Interest in adaptive and distributed systems for routing control in
networks has led to the development of a new class of algorithms,
which is inspired by the "emergent" shortest path finding behaviours
observed in biological ant colonies. This class utilizes ant-like
agents,
which autonomously traverse the network and collectively construct a
distributed routing policy. Agent-based routing algorithms belonging to
this class do not require a complete model of the network, and are able
to adapt autonomously to network changes in dynamic and unpredictable
environments. Some important aspects and limitations of this class
of algorithms can be modelled and understood in the context of Markov
decision problems, reinforcement learning, and game theory. We present
an analytic modelling approach for agent-based routing algorithms, and
in
particular, we discuss the effect that randomized exploration
strategies
can have on the routing policies that are generated by the agents.
[talk]
- Kelly Fleetwood
An Introduction to Differential Evolution
Abstract:
Differential Evolution (DE) was introduced in 1996 by Price and
Storn. It is a stochastic, population-based optimisation method that
belongs to the class of Evolutionary Algorithms. It can be used to
minimise real, integer, discrete and mixed parameter functions and it
has recently been applied to problems in engineering, chemistry and
agriculture. On classic optimisations test problems it has been shown
to be more efficient than annealing methods and genetic
algorithms. This talk provides a thorough introduction to the basic
Differential Evolution algorithm including an example of its
performance.
[talk|movie]
- Michael Gagen
Non-myopic multi-player optimization with application to
the iterated prisoner's dilemma
Abstract:
In 1944, von Newmann and Morgenstern formalized the functional
optimization algorithms used in game theory, economics and artificial
intelligence. They did this by (essentially) borrowing the functional
optimization methods used in physics where, for instance, actions
are minimized under the assumption that Lagrangians are continuous and
differentiable and functionals of uncorrelated fields to avoid
non-local
outcomes. In developing their strategic economic optimization
algorithms,
von Newmann and Morgenstern likewise assumed that the functionals to be
optimized were continuous and differentiable and uncorrelated. This is
essentially the myopic agent assumption. However, economic rational
players are not electrons, and can exploit correlations to render their
optimization space non-continuous and non-differentiable. In this work,
we drop the myopic assumption arbitrarily imposed by von Neumann and
Morgenstern, and demonstrate that non-myopic optimization leads to
rational cooperation in the iterated prisoner's dilemma in contrast
to myopic optimization outcomes insisting that defection is the sole
rational choice of play.
[talk]
- Marcus Gallagher
Explicit modelling in metaheuristic optimization
Abstract:
Statistical modelling and Machine Learning methods have seen some
application to solving optimization problems. In general, this
involves explicitly modelling the data produced by a search algorithm,
and using the model (e.g) to increase the speed of the search, to find
better solutions, or to gain insight into the problem by examining
the model produced. In the field of Metaheuristics (inc. Evolutionary
Computation), density estimation techniques and probabilistic graphical
models have been used to perform model-based optimization. This work
is usually referred to as Estimation of Distribution Algorithms (EDAs).
Model-based optimization has also been considered outside the machine
learning community (e.g, using response surfaces).
In this talk I will mention briefly some of the existing
approaches
in learning-based optimization algorithms. In particular, I will
describe the mechanisms of some well-known EDAs. I will also mention
one approach to constructing a framework for EDAs, based on
minimization
of the KL-divergence between the model (probability distribution) and a
distribution that depends on the objective function of the optimization
problem.
[talk]
- Jonathan Keith
Sequence alignment by rare event simulation
Abstract:
I present a new stochastic method for finding the optimal alignment of
DNA sequences. The method works by generating random paths through a
graph (the edit graph) according to a Markov chain. Each path is
assigned a score, and these scores are used to modify the transition
probabilities of the Markov chain. This procedure converges to a fixed
path through the graph, corresponding to the optimal (or near-optimal)
sequence alignment. The rules with which to update the transition
probabilities are based on the Cross-Entropy Method, a new technique
for
stochastic optimization. This leads to very simple and natural updating
formulas. Due to its versatility, mathematical tractability and
simplicity, the method has great potential for a large class of
combinatorial optimization problems, in particular in biological
sciences.
[talk]
- Dirk Kroese
The Cross-Entropy Method and mathematical programming
Abstract:
Many practical problems in Science involve solving complicated
mathematical programming questions, including multi-extremal
continuous, mixed-integer and constrained optimisation problems. The
Cross-Entropy (CE) method [1] gives a versatile and powerful new
approach to solving these problems.
In this talk I will explain how the CE method works. I
will start with
a simple example in rare event simulation, which will explain the
concept of cross-entropy and motivate the optimisation idea behind the
CE method. I will then illustrate the simplicity and elegance of the
method through various easy examples in continuous multi-extremal and
combinatorial optimisation.
[1] Rubinstein, R.Y. and Kroese, D.P. (2004) The Cross-Entropy Method:
A Unified Approach to Combinatorial Optimization, Monte Carlo
Simulation and Machine Learning, Springer-Verlag, New York.
[talk]
- Alex Smola
Exponential families in feature space
Abstract:
In this talk I will discuss how exponential families, a standard tool
in
statistics, can be used with great success in machine learning to unify
many existing algorithms and to invent novel ones quite effortlessly.
In
particular, I will show how they can be used in feature space to
recover
Gaussian Process classification for multiclass discrimination, sequence
annotation (via Conditional Random Fields), and how they can lead to
Gaussian Process Regression with heteroscedastic noise assumptions.
[talk]
- Thomas Taimre
Application of the Cross-Entropy Method to clustering and
vector
quantization
Abstract:
We apply the Cross-Entropy (CE) method to problems in clustering and
vector quantization. Through various numerical experiments we
demonstrate
the high accuracy of the CE algorithm and show that it can generate
near-optimal clusters for fairly large data sets. We compare the CE
method with well-known clustering and vector quantization methods such
as K-means, fuzzy K-means and linear vector quantization. Each method
is applied to benchmark and image analysis data sets for this
comparison.
[talk]
Participants
|
Name |
Email/Web |
Affiliation |
|
|
|
|
|
Habib Alehossein |
h.alehossein at minmet.uq.edu.au
|
University of Queensland
|
|
David Ball
|
dball at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Josh Bartlett
|
s4079103 at student.uq.edu
|
University of Queensland
|
|
Mikael Boden
|
mikael at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Zdravko Botev
|
botev at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
Michael Bulmer
|
mrb at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
Ben Cairns |
bjc at maths.uq.edu.au
|
MASCOS, University of Queensland
|
|
Andre Costa |
A.Costa at ms.unimelb.edu.au
|
MASCOS, University of Melbourne
|
|
David De Wit
|
dr_david_de_wit at yahoo.com.au
|
Department of Mathematics,
University of Queensland
|
|
Jennifer Dodd
|
jdodd at physics.uq.edu.au
|
Department of Physics, University of Queensland
|
|
Geoffery Ericksson |
g.ericksson at uq.edu.au |
ACMC/Queensland Brain Institute,
University of Queensland
|
|
Michael Gagen
|
m.gagen at imb.uq.edu.au
|
IMB, University of Queensland
|
|
Marcus Gallagher
|
marcusg at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Rossen Halatchev
|
r.halatchev at uq.edu.au
|
CRC Mining,
University of Queensland
|
|
Johan Hawkins |
jhawkins at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Xiaodi Huang
|
huangx at usq.edu.au
|
Department of Mathematics & Computing,
University of Southern Queensland
|
|
Jonathan Keith
|
j.keith1 at mailbox.uq.edu.au
|
Department of Mathematics, University of Queensland
|
|
Dirk Kroese
|
kroese at maths.uq.edu.au
|
Department of Mathematics, University of Queensland
|
|
Naveen Kumar
|
naveen at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Dharma Lesmono |
dlesmono at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
Peter Lindsay |
Peter.Lindsay at accs.uq.edu.au
|
ARC Centre for Complex Systems,
University of Queensland
|
|
Stefan Maetschke
|
www.itee.uq.edu.au/~stefan
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Alana Moore
|
a.moore at epsa.uq.edu.au
|
Sustainable Minerals Institute,
University of Queensland
|
|
Sho Nariai
|
sho at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
Phil Pollett
|
pkp at maths.uq.edu.au
|
MASCOS, University of Queensland
|
|
Alex Pudmenzky
|
a.pudmenzky at mailbox.uq.edu.au
|
University of Queensland
|
|
Tony Roberts
|
aroberts at usq.edu.au
|
Department of Mathematics & Computing,
University of Southern Queensland
|
|
Peter Robinson |
pjr at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
David Rohde
|
djr at physics.uq.edu.au
|
Department of Physics, University of Queensland
|
|
Joshua Ross
|
jvr at maths.uq.edu.au
|
MASCOS, University of Queensland
|
|
Mark Seeto
|
mbs at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
David Sirl
|
dsirl at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
Alex Smola
|
Alex.smola at anu.edu.au
|
NICTA, ANU
|
|
Thomas Taimre
|
ttaimre at maths.uq.edu.au
|
MASCOS, University of Queensland
|
|
Liam Wagner
|
ldw at maths.uq.edu.au
|
Department of Mathematics,
University of Queensland
|
|
Xiong Wang
|
jxw at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
Queensland University of Technology
|
|
Tim Waterhouse |
thw at maths.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Geoffrey Watson
|
gwat at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Riyu Wei |
rywei at acmc.uq.edu.au
|
ACMC,
University of Queensland
|
|
Bill Whiten |
W.Whiten at uq.edu.au
|
Julius Kruttschnitt Mineral Research Centre,
University of Queensland
|
|
Ian Wood
|
i.wood at qut.edu.au |
School of Mathematical Sciences,
Queensland University of Technology
|
|
Yanliang (Laurel) Yu |
s4064477 at student.uq.edu.au
|
University of Queensland
|
|
Bo Yuan |
boyuan at itee.uq.edu.au
|
School of Information Technology & Electrical Engineering,
University of Queensland
|
|
Justin Xi Zhu |
j.zhu at imb.uq.edu.au
|
IMB, University of Queensland
|
|
Karla Ziri-Castro
|
ziricast at usq.edu.au
|
Department of Mathematics & Computing,
University of Southern Queensland
|
|