Integrating Logic and Statistics
Novel Algorithms in Markov Logic Networks
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Integrating logic and probability has a long story inArtificial Intelligence and Machine Learning. Thisbook attempts the challenge of exploring anddeveloping high performing algorithms for astate-of-the-art model that integrates first-orderlogic and probability. However, much remains to bedone until AI systems will reach human intelligence.A powerful language to achieve this is Markov Logicwhich embodies the experience and successes ofvarious subfields of AI and Statistics. It allows toexpress complexity and uncertainty, just as humanswould do in complex environments. Moreover, complexmodels t...
Integrating logic and probability has a long story in
Artificial Intelligence and Machine Learning. This
book attempts the challenge of exploring and
developing high performing algorithms for a
state-of-the-art model that integrates first-order
logic and probability. However, much remains to be
done until AI systems will reach human intelligence.
A powerful language to achieve this is Markov Logic
which embodies the experience and successes of
various subfields of AI and Statistics. It allows to
express complexity and uncertainty, just as humans
would do in complex environments. Moreover, complex
models that reflect real-world phenomena can be
learned efficiently from examples and powerful
inference algorithms can be used to answer queries
about the world. This book makes an effort towards
building powerful algorithms for these two tasks.
Thus it is hoped that it will constitute another step
forward in our attempt to better understand and build
intelligent systems.
Artificial Intelligence and Machine Learning. This
book attempts the challenge of exploring and
developing high performing algorithms for a
state-of-the-art model that integrates first-order
logic and probability. However, much remains to be
done until AI systems will reach human intelligence.
A powerful language to achieve this is Markov Logic
which embodies the experience and successes of
various subfields of AI and Statistics. It allows to
express complexity and uncertainty, just as humans
would do in complex environments. Moreover, complex
models that reflect real-world phenomena can be
learned efficiently from examples and powerful
inference algorithms can be used to answer queries
about the world. This book makes an effort towards
building powerful algorithms for these two tasks.
Thus it is hoped that it will constitute another step
forward in our attempt to better understand and build
intelligent systems.
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