
Report on MASCOS OneDay Symposium
MultiAgent 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 MultiAgent 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 "antlike" mobile agents 

10:00 
Kelly Fleetwood 
An introduction to differential evolution 

10:30 
Break 
[Refeshments provided] 

10:45 
Michael Gagen 
Nonmyopic multiplayer optimization with application
to the iterated prisoner's dilemma 

11:15 
Dirk Kroese 
The CrossEntropy 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 CrossEntropy Method to clustering
and vector quantization 

16:15 
Close 
Abstracts
 Andre Costa
Exploration, robustness and optimality of network routing
algorithms which employ "antlike" 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 antlike
agents,
which autonomously traverse the network and collectively construct a
distributed routing policy. Agentbased 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 agentbased 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, populationbased 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.
[talkmovie]
 Michael Gagen
Nonmyopic multiplayer 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
nonlocal
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 noncontinuous and nondifferentiable. In this work,
we drop the myopic assumption arbitrarily imposed by von Neumann and
Morgenstern, and demonstrate that nonmyopic 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 modelbased optimization. This work
is usually referred to as Estimation of Distribution Algorithms (EDAs).
Modelbased 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 learningbased optimization algorithms. In particular, I will
describe the mechanisms of some wellknown EDAs. I will also mention
one approach to constructing a framework for EDAs, based on
minimization
of the KLdivergence 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 nearoptimal)
sequence alignment. The rules with which to update the transition
probabilities are based on the CrossEntropy 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 CrossEntropy Method and mathematical programming
Abstract:
Many practical problems in Science involve solving complicated
mathematical programming questions, including multiextremal
continuous, mixedinteger and constrained optimisation problems. The
CrossEntropy (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 crossentropy 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 multiextremal and
combinatorial optimisation.
[1] Rubinstein, R.Y. and Kroese, D.P. (2004) The CrossEntropy Method:
A Unified Approach to Combinatorial Optimization, Monte Carlo
Simulation and Machine Learning, SpringerVerlag, 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 CrossEntropy Method to clustering and
vector
quantization
Abstract:
We apply the CrossEntropy (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
nearoptimal clusters for fairly large data sets. We compare the CE
method with wellknown clustering and vector quantization methods such
as Kmeans, fuzzy Kmeans 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 ZiriCastro

ziricast at usq.edu.au

Department of Mathematics & Computing,
University of Southern Queensland

