17,104 research outputs found
A Minimal Architecture for General Cognition
A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International
Joint Conference on Neural Network
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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