Description:
Machine learning builds models of the world using training data from
the application domain and prior knowledge about the problem. The models
are later applied to future data in order to estimate the current state
of the world. An implied assumption is that the future is
stochastically similar to the past. The approach fails when the system
encounters situations that are not anticipated from the past experience.
In contrast, successful natural organisms identify new unanticipated
stimuli and situations and frequently generate appropriate responses.
The observation described above lead to the initiation of the DIRAC EC
project in 2006. In 2010 a workshop was held, aimed to bring together
researchers and students from different disciplines in order to present
and discuss new approaches for identifying and reacting to unexpected
events in information-rich environments. This book includes a summary of
the achievements of the DIRAC project in chapter 1, and a collection of
the papers presented in this workshop in the remaining parts.
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