Andere Kunden interessierten sich auch für
![Statistics of Images for Feedback in Vision]( "Statistics of Images for Feedback in Vision")
Statistics of Images for Feedback in Vision38,99 €![Hybrid AI Models for the Characterization of Oil and Gas Reservoirs]( "Hybrid AI Models for the Characterization of Oil and Gas Reservoirs")
Hybrid AI Models for the Characterization of Oil and Gas Reservoirs38,99 €![Emotions: psychology, logical formalisation, and agent applications]( "Emotions: psychology, logical formalisation, and agent applications")
Emotions: psychology, logical formalisation, and agent applications51,99 €![Musical Machines]( "Musical Machines")
Musical Machines32,99 €![Learning to Co-operate in Multi-Agent Systems]( "Learning to Co-operate in Multi-Agent Systems")
Learning to Co-operate in Multi-Agent Systems38,99 €![A Graph Planning Procedure within an Agent Architecture]( "A Graph Planning Procedure within an Agent Architecture")
A Graph Planning Procedure within an Agent Architecture44,99 €![USING FUZZY PROBABILITY AND SIMULATION METHODS IN WEB PLANNING MODELS]( "USING FUZZY PROBABILITY AND SIMULATION METHODS IN WEB PLANNING MODELS")
USING FUZZY PROBABILITY AND SIMULATION METHODS IN WEB PLANNING MODELS32,99 €
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.