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Vector Symbolic Architectures (VSA) are approaches to representing symbols and structured combinations of symbols as high-dimensional vectors. They have applications in machine learning and for understanding information processing in neurobiology. VSAs are typically described in an abstract mathematical form in terms of vectors and operations on vectors. In this work, we show that a machine learning...
The ability to reason over large scale data and return responsive query results is widely seen as a critical step to achieving the Semantic Web vision. We describe an approach for partitioning OWL Lite datasets and then propose a strategy for parallel reasoning about concept instances and role instances on each partition. The partitions are designed such that each can be reasoned on independently...
The paper proposes a system architecture for artificial creativity that enables a robot to perform portraits. The proposed cognitive architecture is inspired by the PSI model, and it requires that the motivation of the robot in the execution of its tasks is influenced by urges. Such parameters depend on both internal and external evaluation mechanisms. The system is a premise for the development of...
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