Davide Nitti will defend his dissertation "Hybrid Probabilistic Logic Programming".
The public PhD defense will take place on Friday August 26, 2016 at 1:30 PM in the Auditorium (01.07), Arenbergkasteel, Kasteelpark Arenberg 1, 3001 Heverlee.
An important issue in artificial intelligence and many other fields is
modeling the domain of interest.
Given a model it is possible to perform inference to answer questions of
interest, or make decisions to maximize a given utility.
An active research topic concerns declarative languages for modeling and
learning a wide range of applications.
In particular, probabilistic logic programming combines
first-order-logic with probability theory to model uncertainty.
However, the majority of such languages do not support continuous random
variables, or their support for continuous variables is limited.
In this thesis we address this issue extending probabilistic logic
programming techniques to deal
with hybrid relational domains, involving both discrete and continuous
We first propose a new inference algorithm for the language of
Distributional Clauses, that supports zero-probability evidence,
including algebraic constraints for which most frameworks fail.
Secondly, we extend the algorithm for filtering in temporal domains.
Finally, we propose a planner to solve Markov decision Processes
described with Distributional Clauses.
The proposed algorithms are tested in several synthetic and real-world
problems showing that they are competitive with respect to the state of
In particular, we showed how the framework can be used to exploit
relational and continuous information jointly to improve state
estimation in robotics and vision applications.