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Building intelligent computer assistants has been a
long-cherished goal of
AI. Many intelligent assistant systems were built
and fine-tuned to specific
application domains. In this work,
we develop a general model of assistance that
combines
three powerful ideas: decision theory, hierarchical
task models and
probabilistic relational languages. We use the
principles of decision theory
to model the general problem of intelligent
assistance. We use a
combination of hierarchical task models and
probabilistic relational
languages to specify prior knowledge of the computer
assistant....

This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review...