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Lab Lunch Talk by Anastasis Georgoulas

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Machine Learning for Formal Dynamical Systems

  • Lab Lunch
When Nov 11, 2014
from 01:00 PM to 02:00 PM
Where MF2
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Formal methods, such as process algebras, have been demonstrated to be useful frameworks for the modelling and analysis of complex systems. Finding parametrizations for these models that match expected or observed behaviour is an open problem, however, with solutions often being time-consuming and ad-hoc. Algorithms from the field of machine learning, on the other hand, can be used to extract information from observed data and manage uncertainty in models in a statistically consistent way. I will be talking about our efforts to bring together the two fields, including ProPPA, a probabilistic programming process algebra that incorporates uncertainty in the description of the system and its semantics. This is joint work with Jane Hillston and Guido Sanguinetti.

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