Belief updating and learning in semi qualitative probabilistic networks

My main contribution to the Lunar Rover project was the Viper system, a piece of technology that was used in the Atacama desert for tests.

The Viper system, estimates position from a stream of images, by matching images to a previously constructed map of the environment.

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 at the time). Here are two representative papers, perhaps of historic value: , 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. I worked, for two years, in the Lunar Rover project during my Ph D years at Carnegie Mellon.

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 in: I also worked on a variety of other problems.

A summary can be found in: In the process of putting together Java Bayes, I have developed a very general, yet easy to understand, inference algorithm for Bayesian networks.

Probabilistic logic with independence, International Journal of Approximate Reasoning, v. I have been interested, together with several students, in languages for knowledge representation and machine learning that combine graph-based modeling, in particular Bayesian networks, with logical structures, in particular description logics.

This paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform Bayesian network parameter estimation.

For complete data, a global optimum solution to maximum likelihood estimation is obtained in polynomial time, while for incomplete data, a modified expectation-maximization method is proposed.

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 ... I'm a founding member of the Society for Imprecise Probability Theory and Applications; I also helped organize some of the International Symposium for Imprecise Probabilities and Their Applications (ISIPTA) and edited some of its proceedings.

I have developed graph-based models that represent sets of probability measures over sets of variables; these are often called Fabio G. Still on concepts of independence, I have considered such concepts in the realm of full conditional measures (that is, measures that extend standard probability by adopting conditional probability as the primary object of interest, and hence allowing conditioning on events of probability zero): I have also looked at sequential decision making (that is, planning) under uncertainty.

A former advisee involved with this project, Marco Ackermann, received the prize of Best Master Thesis in Mechanical Engineering in Brazil 2003, granted by the Brazilian Association for the Mechanical Sciences (ABCM), for this work.

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