Unified theories of cognition
Learning to solve multiple goals
Learning to solve multiple goals
Being There: Putting Brain, Body, and World Together Again
Being There: Putting Brain, Body, and World Together Again
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Temporal Difference Model Reproduces Anticipatory Neural Activity
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
An architecture for vision and action
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Multiple-goal reinforcement learning with modular Sarsa(O)
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Modularity and design in reactive intelligence
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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To make progess in understanding human brain functionality, we will need to understand its basic functions at an abstract level. One way of accomplishing such an integration is to create a model of a human that has a useful amount of complexity. Essentially, one is faced with proposing an embodied “operating system” model that can be tested against human performance. Recently technological advances have been made that allow progress to be made in this direction. Graphics models that simulate extensive human capabilities can be used as platforms from which to develop synthetic models of visuo-motor behavior. Currently such models can capture only a small portion of a full behavioral repertoire, but for the behaviors that they do model, they can describe complete visuo-motor subsystems at a level of detail that can be tested against human performance in realistic environments. This paper outlines one such model and shows both that it can produce interesting new hypotheses as to the role of vision and also that it can enhance our understanding of visual attention.