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DTSTART:20231029T030000
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UID:calendar.26189.field_data.0@www.corsodrupal.uniroma1.it
DTSTAMP:20240528T105623Z
CREATED:20230708T202141Z
DESCRIPTION:Linear Temporal Logic (LTL) is a modal logic widely used in dif
ferent domains\, such as robotics and Business Process Management\, for sp
ecifying temporal relationships\, dynamic constraints\, and performing aut
omated reasoning. However\, exploiting LTL knowledge in real-world applica
tions can be difficult due to the knowledge's symbolic 'crispy' nature. Th
is seminar explores different techniques to relax the knowledge to make it
applicable in continuous domains where symbols are grounded through Deep
Learning modules and the symbol grounding function and/or the symbolic tem
poral specification can be unknown or partially known. In particular\, we
propose two different techniques: (i) one based on Logic Tensor Networks a
nd (ii) one based on Probabilistic Finite Automaton. We apply the first ap
proach to classifying sequences of images\, and we show that our approach
requires less data and is less prone to overfitting than purely deep-learn
ing-based methods. We use the second approach to learn DFA specifications
from traces with gradient-based optimization\, showing that it can learn l
arger automata and is more resilient to noise in the dataset than prior wo
rk. Finally\, we propose an extension of our second approach that we apply
to non-Markovian Deep Reinforcement Learning problems. This third contrib
ution has shown to be more sample efficient of methods based on Recurrent
Neural Networks\, and\, at the same time\, it requires less prior knowledg
e than methods based on LTL\, such as Reward Machines and Restraining Bolt
s.
DTSTART;TZID=Europe/Paris:20230710T110000
DTEND;TZID=Europe/Paris:20230710T110000
LAST-MODIFIED:20230709T174702Z
LOCATION:Aula Magna
SUMMARY:Seminario pubblico di Elena Umili (Procedura valutativa per n.4 pos
ti di Ricercatore a tempo determinato tipologia A - SC 09/H1 SSD ING-INF/0
5) - Integrating Linear Temporal Logic with Deep-Learning-Based Applicatio
ns - Elena Umili
URL;TYPE=URI:http://www.corsodrupal.uniroma1.it/node/26189
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