EAN: 9783031004216

SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE A...

Most subfields of computer science have an interface layer via which applications communicate
with the infrastructure  and this is key to their success (e.g.  the Internet in networking 
the relational model in databases  etc.). So far this interface layer has been missing in AI.
First-order logic and probabilistic graphical models each have some of the necessary features 
but a viable interface layer requires combining both. Markov logic is a powerful new language
that accomplishes this by attaching weights to first-order formulas and treating them as
templates for features of Markov random fields. Most statistical models in wide use are special
cases of Markov logic  and first-order logic is its infinite-weight limit. Inference algorithms
for Markov logic combine ideas from satisfiability  Markov chain Monte Carlo  belief
propagation  and resolution. Learning algorithms make use of conditional likelihood  convex
optimization  and inductive logic programming. Markov logic has been successfully applied to
problems in information extraction and integration  natural language processing  robot mapping
social networks  computational biology  and others  and is the basis of the open-source Alchemy
system. Table of Contents: Introduction   Markov Logic   Inference   Learning   Extensions  
Applications   Conclusion

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