Research overview
I am Assistant Professor at the Mechatronics group
at the Escola Politecnica,
Universidade de Sao Paulo.
This group is an inter-disciplinary effort to research
in Automation, Robotics, Artificial Intelligence and Industrial
Management (among other things!). I have a background on
Electrical Engineering, and do most of my research in
Computer Science issues.
I am currently working on three different lines of research;
these lines mostly reflect my past and present interests.
Theory of sets of probabilities (credal sets)
First, I explore the
theory of sets of probabilities.
There is not a single, stable name for this theory: some people
use "theory of imprecise probabilities"; others say "theory of
credal sets", or "Quasi-Bayesian theory", or "theory of lower
expectations", or ... several other names. I believe this theory
is the right tool to model statistical uncertainty, and
will ultimately be the unifying foundation for
inference and decision-making.
To know more about the theory, you can look at my
explanation of it,
and also look at web sites for the
First
and the Second
Symposia on Imprecise Probabilities and Their Applications
(which I co-organized) and the
Imprecise
Probabilities Project (which I co-edit).
As a result of the First ISIPTA, a book
and two special issues were edited:
- G. de Cooman, F. G. Cozman, S. Moral, P. Walley (editors).
Proceedings of the First Symposium on Imprecise Probabilities
and Their Applications, ISBN 90-805034-1-X, Ghent, Belgium, 1999.
- F. G. Cozman, S. Moral (editors).
Special issue: Reasoning with Imprecise Probabilities,
International Journal of Approximate Reasoning,
vol. 24, n. 2/3, 2000.
- F. G. Cozman, S. Moral (editors).
Special issue: Models for Imprecise Probabilities
and Partial Knowledge,
International Journal of Uncertainty, Fuzziness and
Knowledge-based Systems, vol. 8, n. 3, 2000.
I work both
on foundational and algorithmic issues (with emphasis on the later).
Basically, I'm interested in efficient algorithms
to obtain posterior quantities; a big part of the
work can be grasped through the papers
- F. G. Cozman.
Credal networks, Artificial Intelligence Journal,
vol. 120, pp. 199-233, 2000.
- F. G. Cozman.
Computing posterior upper expectations,
International Journal of Approximate Reasoning,
vol. 24, pp. 191-205, 2000.
- F. G. Cozman.
Calculation of Posterior Bounds Given Convex Sets of
Prior Probability Measures and Likelihood Functions,
Journal of Computational and Graphical Statistics,
vol. 8(4), pp. 824-838, 1999.
The second paper above contains a summary of the third paper.
Also, the second paper is an improved version of the
following paper, presented at ISIPTA99:
The papers mentioned previously deal, at least partially,
with graphical models associated with
sets of probabilities and judgements of independence. I'm
particularly interested in models that generalize Bayesian
networks (called credal networks).
My ideas on this are summarized in the paper at the
Artificial Intelligence journal. Some preliminary results
in that paper appeared in the following papers (but I recommend
looking at the journal paper, which is better edited and more
mature):
In connection with credal networks, I have developed
algorithms for the
JavaBayes system, where I explore
robust inferences with credal networks, using both local
and global perturbations.
I'm also interested in
concepts and properties of irrelevance/independence connected
to the theory of sets of probabilities. The following papers
look at some basic concepts in this area:
- F. G. Cozman.
Separation
Properties of Sets of Probability Measures.
XVI Conference on Uncertainty in Artificial Intelligence,
pp. 107-115, San Francisco, California, July 2000.
- F. G. Cozman.
Irrelevance and Independence Axioms in Quasi-Bayesian Theory,
European Conference on Symbolic and Quantitative Approaches to
Reasoning with Uncertainty (ECSQARU),
London, England, published in Symbolic
and Quantitative Approaches to Reasoning with Uncertainty,
A. Hunter e S. Parsons (eds.),
pp. 128-136, Springer, July, 1999.
I have also pursued some different directions, looking
at the problem of
sequential-decision
making associated with observations, and also exploring
the possibility of
learning convex sets of probability from data:
Another idea I have pursued is the possibility of learning
convex sets of distributions from data:
Bayesian networks
Second, I am quite interested in graphical models for
uncertainty modeling, particularly Bayesian networks.
I develop the
JavaBayes
system, a general purpose inference engine for graphical models;
the engine can generate posterior
probabilities and expectations for probabilistic models represented as
directed acyclic graphs. The system is distributed freely
(under the GNU license) in the spirit of fostering teaching and
research. JavaBayes
is now used in many university and research labs around the
world. A summary is:
- F. G. Cozman.
The JavaBayes system,
The ISBA Bulletin, vol. 7, n. 4, pp. 16-21, 2001
(invited publication without referreing process).
In the process of putting together JavaBayes, I have
developed a very general, yet easy to understand, inference
algorithm for Bayesian networks. The method generalizes
the variable elimination algorithm, and is suited for
teaching due to its simplicity. You can get it:
While
JavaBayes
is a complete system, with graphical interface, parsers, etc,
I've been investigating a system that is more geared towards
the needs of embedded systems. The
EBayes
project is an effort to produce a lightweight Bayesian network
engine that is appropriate to the growing market of embedded
devices. Some preliminary tests are described at
- F. T. Ramos, F. Mikami, F. G. Cozman.
Implementação de Redes Bayesianas em Sistemas
Embarcados.
Proceedings of the IBERAMIA/SBIA 2000 Workshops
(Workshop on Probabilistic Reasoning in Artificial
Intelligence), pp. 65-69, Editora Tec Art, São
Paulo, Brazil, 2000.
Finally, I am interest in applying sensitivity analysis
techniques (from the realm of robust Statistics) to Bayesian
networks. I have been developing techniques that use the
theory of sets of probabilities as a tool for the assessment
of sensitivity in graphical statistical models.
A preliminary effort is:
- F. G. Cozman.
Sensitivity and Robustness Analysis of Bayesian Networks,
IV Simpósio Brasileiro de Automação Industrial,
pp. 251-255, São Paulo, São Paulo, Brazil, September, 1999.
Robotics: Teleoperation, mobile robots, automated orthosis...
Third, I really like to work with mobile robots and similar
devices. I have always tried to spend some time building new
friends out of motors and computers.
Right after my undergraduate course, I took a Master of Engineering
in Brazil, and worked in the first brazilian mobile robot, called Ariel.
We produced a complete system, from the mechanical structure to the
planning software; the result was very impressive and we ended up
showing it off in the Jornal da Globo (Brazil's second most important
TV news source). Unfortunately, that material is not online.
Here are two significant papers, perhaps of historic value:
- F. G. Cozman; P. E. Miyagi. Trajectory Controller for a Mobile
Robot using Optimal Control, XI Congresso Brasileiro de Engenharia
Mecânica, 3:537-540, São Paulo, SP Brasil, 1991.
- J. C. Adamowski; M. G. Simões; F. G. Cozman. Desenvolvimento
de um Robô Móvel, VIII
Congresso Brasileiro de Automática,
Belém, 1990; selected for IV Congreso Latinoamericano de
Control Automatico, Puebla Mexico, 1990; also presented at
IV Congresso Nacional de
Automação
Industrial, pp. 209-212, São Paulo, SP Brasil, 1990.
I worked, for two years, in the Lunar Rover project
during my PhD years at Carnegie Mellon.
My main contribution to the Lunar Rover project was the
Viper system, a piece of technology
that was used in the Atacama
mission. The Viper system,
estimates position from a stream of images, by matching images
to a previously constructed map of the environment.
The estimator builds an occupancy map for the position
of the robot; the catch is that the occupancy maps actually represents
a full density ratio familiy of distributions which generate both
the estimates and the confidence on the estimates.
The system is described at
- F. G. Cozman, E. Krotkov, C. E. Guestrin.
Outdoor Visual Position Estimation for Planetary Rovers,
Autonomous Robots, vol. 9, pp. 135-150, 2000.
There is also a description of an old version of the
Viper system at
- F. Cozman; E. Krotkov.
Automatic
Mountain Detection
and Pose Estimation for Teleoperation of Lunar Rovers,
Proc. of the International Conference on Robotics
and Automation, pp. 2452-2457, Albuquerque, New Mexico, 1997.
Also published in
Experimental Robotics V,
Lecture Notes in Control and Information Sciences 232,
pp. 207-215,
Alicia Casals e Anibal T. de Almeida (eds.),
Barcelona, Spain, June (15-18) 1997.
The vision algorithms developed for the Viper system
reported in the following papers.
- C. Guestrin; F. G. Cozman; E. Krotkov.
Fast Software Image Stabilization with Color Registration,
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
pp. 19-24, Victoria, Canada, October, 1998.
- C. Guestrin; F. G. Cozman; M. G. Simões.
Industrial Applications of Image Mosaicing and Stabilization,
Second International Conference on Knowledge-based Intelligent
Electronic Systems, pp. 174-183, Adelaide, Australia, April 1998.
During a few years at CMU I worked with the
Ratler
robot. We actually had it rolling for some fifty
kilometers in our outdoor tests; you can take a look
at the following paper.
- R. Simmons; E. Krotkov; L. Chrisman; F. Cozman; R. Goodwin;
M. Hebert; L. Katragadda; S. Koenig; G. Krishnaswamy; Y. Shinoda; W.
Whittaker; and P. Klarer.
Experience with Rover Navigation for Lunar-Like Terrains,
Proceedings of the Conference on Intelligent Robots
and Systems (IROS), pages 441-446, 1995.
Right now I'm not working too much with mobile robots.
I have some undergraduate students designing a small-sized robot
for visual inspection of hazardous environments, as part
of a larger project on teleoperation of industrial
environments. Hopefully I
will report on this work in the near future.
Instead of focusing on mobile robots, I'm increasingly
focusing on automated orthosis (devices that can help
people with physical disabilities). This is ongoing work,
and I also hope to report on this in the near future.
I also worked on a few other problems.
Some years
ago I produced a line
linker based on the Akaike Information Criterion (AIC),
which was distributed in the net. That code is probably too old to
be of interest, but the algorithm using the AIC may be of value; there
is a tech report that
describes it.
Another aspect of my work was the investigation of
celestial data as a source
of position estimates for mobile robots:
And finally, another twist
in this work was the study of atmospheric scattering as a
clue for depth in outdoor environments; as far as I know, the first
study of scattering in the context of image understanding.
- F. Cozman; E. Krotkov.
Depth from
Scattering,
Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition, Puerto Rico, June, 1997.
I have been interested for some time in the problem of calculating
bounds for dynamical systems; I have since discovered a huge literature
in this area, which I expect to be of great relevance for robust
Statistics in the future. I have published some work on the specific
topic of manipulating
ellipsoidal models of error in Robotics.
fgcozman@cs.cmu.edu [Send Mail?]