Knowledge Configuration for AI solvers

Speaker:  Mauro Vallati – Huddersfield, United Kingdom
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


Given an off-the-shelf solver and a symbolic representation of a problem to be solved, a way for improving performance is the configuration of the parameters of the solver. Such parameters allow to control the behaviour of the solver, that can be tuned to the specific kind of problems at hand. On the other hand, it is also possible to configure the way in which the symbolic model is presented to the solver, in order to affect its performance. This lecture provides an overview of the knowledge configuration approach, with examples from the areas of SAT, AI Planning, and Abstract Argumentation.

About this Lecture

Number of Slides:  40
Duration:  45 minutes
Languages Available:  English, Italian
Last Updated: 

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