To handle the complexity and uncertainty present in most real-world problems, we need AI that is both logical and
statistical, integrating first-order logic
and graphical models. One or the other
FOR MANY YEARS, the two dominant paradigms in
Markov logic can be used as a general
artificial intelligence (AI) have been logical AI and
statistical AI. Logical AI uses first-order logic and
related representations to capture complex
relationships and knowledge about the world.
However, logic-based approaches are often too brittle
to handle the uncertainty and noise present in many
applications. Statistical AI uses probabilistic
representations such as probabilistic graphical
models to capture uncertainty. However, graphical
models only represent distributions over
propositional universes and must be customized to
handle relational domains. As a result, expressing
complex concepts and relationships in graphical models
is often difficult and labor-intensive.
framework for joining logical and statistical AI.
BY PEDRO DOMINGOS AND DANIEL LOWD
˽ Intelligent systems must be able to handle
the complexity and uncertainty of the real
world. Markov logic enables this by unifying
first-order logic and probabilistic graphical
models into a single representation. Many
deep architectures are instances of
˽ A extensive suite of learning and
inference algorithms for Markov logic has
been developed, along with open source
implementations like Alchemy.
˽ Markov logic has been applied to natural
language understanding, information
extraction and integration, robotics,
social network analysis, computational
biology, and many other areas.