[DL] 5 Ph.D positions at the DKM research unit of Fondazione Bruno Kessler

[Loris Bozzato]

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We propose 5 Ph.D positions on cutting edge research topics in Artificial
Intelligence by the Data and Knowledge Management research unit of the
Center of Information Technology of Fondazione Bruno Kessler - Trento.
Italy. For any additional information please don’t hesitate to contact
Luciano Serafini (serafini at fbk.eu)

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Bayesian reasoning for statistical relational learning (2 positions)

Current approaches in statistical relational learning are based on
undirected graphical models such as Markov Logic Networks. State of the art
algorithms for statistical inference cover the Maximum Likelihood (ML) and
Maximum a Posteriori (MAP) tasks, but not so much attention has been
devoted to Bayesian Inference. Due to the high complexity of the models
that can be generated, statistical inference is approximated using sampling
methods. Recently, we proposed a study about Bayesian Inference in hybrid
graphical models (i.e., models composed of discrete and continuous random
variables); the advantage of Bayesian inference is that, it’s a truly
statistical inference and it is very robust to overfitting training data.
We design a variational method to solve the “exact inference”. However, to
perform Bayesian inference, combinatorial problems on the discrete
variables must be solved in a more efficient way, and this is still an open
problem. The objective of this thesis, is to extend such proposals and to
make scalable.

CONTACT and INFO: Luciano Serafini serafini at fbk.eu

SCHOOL: University of Trento - Doctoral Program in Information and
Communication Technology

DEADLINE: May 20, 2019, hrs. 04.00 PM (Italian time, GMT +2)

NOTE: In the application specify that you are interested in the Project
Specific Grants (reserved topic scholarships)  A7 - Bayesian reasoning for
statistical relational learning

APPLICATION SITE:
https://ict.unitn.it/education/admission/call-for-application


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Incremental learning of abstract planning models via acting in a real
environment (2 positions)

Autonomous agents, such as robots, chat-bots, self-driving cars, soft-bots
etc., need to plan their actions in order to achieve their goals. For this
reason, they should know the environment in which they operate and the
effects of their actions on the environment. These information are usually
encoded in the so-called “planning domain”, which, need to be “programmed
off line” when the agent is programmed. However, the environment is dynamic
and can have unpredicted changes; therefore, the agent should be able to
adapt to unexpected situations. Furthermore, the effect of actions could be
vary complex and unknown since the beginning; the agents should be able to
learn action effects while acting. The objective of the Ph.D is to develop
the necessary theory and the algorithms that allow an agent to
incrementally learn a discrete, compact, and semantically rich
representation of the planning  domain in an environment in which it is
supposed to interact. This representation is formulated in a form of a
discrete planning domain.

CONTACT AND INFO: Luciano Serafini serafini at fbk.eu and Paolo Traverso
traverso at fbk.eu

POSITION 1 at University of Trento - Doctoral Program in Information and
Communication Technology

        • DEADLINE: May 20, 2019, hrs. 04.00 PM (Italian time, GMT +2)
        • NOTE: In the application specify that you are interested in the
Project Specific Grants (reserved topic scholarships)  A6 - Incremental
learning of abstract planning models via acting in a real environment

        • APPLICATION SITE:
https://ict.unitn.it/education/admission/call-for-application

POSITION 2 at University of Padova - PhD Course: BRAIN, MIND AND COMPUTER
SCIENCE in agreement with Fondazione Bruno Kessler

        • DEADLINE: 14 MAY 2019, 13:00 CEST
        • NOTE: In the application specify the priority on the Scholarship
- Incremental learning of abstract planning models via acting in a real
environment (see figure 7 of the document describing application
instructions)

        • APPLICATION SITE: http://hit.psy.unipd.it/BMCS/admission


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Default in contextualized knowledge representation

Representation of context has been one of the main approaches in the
knowledge representation and reasoning area for the management of large
knowledge bases: here, modelling and reasoning on knowledge is relativized
with respect to the contexts (situations, set of hypothesis) in which it is
supposed to hold. Introducing forms of non-monotonic and default reasoning
in logic-based contextual frameworks is a challenging research issue which,
in particular, plays a role in the propagation and inheritance (with
overriding) of knowledge across hierarchies of contexts.
The objective of this PhD will be to investigate the possibilities for
extending the theory and implementation of the current methods for
representing default information in contextual frameworks and explore their
applications.

CONTACT and INFO: Luciano Serafini serafini at fbk.eu and Loris Bozzato
bozzato at fbk.eu

SCHOOL: University of Trento - Doctoral Program in Information and
Communication Technology

DEADLINE: May 20, 2019, hrs. 04.00 PM (Italian time, GMT+2)

NOTE: In the application specify that you are interested in the Project
Specific Grants (reserved topic scholarships)  C3 - Default in
contextualized knowledge representation

APPLICATION SITE:
https://ict.unitn.it/education/admission/call-for-application




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Understanding multimedia with the help of background knowledge

This phd has the objective of extracting events from commented videos
exploiting background knowledge available in the semantic web. This phd
should develop a holistic approach, where the process of extracting
information from the video, and from the associated text are integrated and
can affect each other at any stage. This implies that video stream and
textual stream are considered as a whole information space and their
interpretations are not independent. Furthermore, video-text interpretation
should not happen in the knowledge vacuum, but it should exploit the
existing large amount of background knowledge available in the semantic web
under the form of ontologies and RDF data. Nowadays--in contrast with the
early years of AI when knowledge acquisition was a bottleneck--large amount
of commonsense knowledge is available in the semantic web, but it cannot be
easily exploited by the state-of-the-art approaches to video and text
analisys. The thesis should investigate on how to extend and adapt
algorithms for video and text analysis in order to inject background
knowledge. The thesis, to reach it's objective, should combine techniques
in machine learning--for processing low level data--with automated
reasoning--to manage with high level semantic knowledge.

CONTACT and INFO: Luciano Serafini serafini at fbk.eu

SCHOOL: University of Padova - PhD Course: BRAIN, MIND AND COMPUTER SCIENCE
in agreement with Fondazione Bruno Kessler

DEADLINE: 14 MAY 2019, 13:00 CEST

NOTE: In the application specify the priority on the Scholarship -
Understanding multimedia with the help of background knowledge(see figure 7
of the document describing application instructions)

APPLICATION SITE: http://hit.psy.unipd.it/BMCS/admission
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