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• W1460162690 abstract "Publisher Summary A crucial problem to solve in a real-time model of behavior is that of interrupts: How does one know when to change the current agenda? One idea is that of completely predicting aspects of the environment. If an agent could predict its perceptions in a literal way, then deviations from these predictions could be used to change focus. The huge complicating issue is that perceptions are hugely vary from instance to instance owing in part to view variations, and therefore, perhaps arguably impossible to predict. However, this problem could be tackled with time-varying histograms and linear prediction methods. In order to understand the role of the constraints of the human body in cognition, one must have a working human body. Standard humanoid graphics model have a very restricted repertoire of movements. One needs accurate physical models of reaching and grasping in order to simulate how long they take and the errors they make. Understanding such movements is a big problem, however a lightweight approach is chosen that generates movements using equilibrium points (EP) and Jacobian approximations. With these a small number of parameter movements are generated that are both realistic enough to be useful and compact enough to be accessible to expected reward methods." @default.
• W1460162690 created "2016-06-24" @default.
• W1460162690 creator A5002822166 @default.
• W1460162690 date "2008-01-01" @default.
• W1460162690 modified "2023-09-25" @default.
• W1460162690 title "Animate Vision, Virtual Environments, and Neural Codes" @default.
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• W1460162690 doi "https://doi.org/10.1016/b978-0-08-046616-3.00008-6" @default.
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