CogXProject reference: 215181
Funded under: FP7-ICT
Cognitive Systems that Self-Understand and Self-Extend [Print to PDF] [Print to RTF]
Total cost:EUR 8 846 820
EU contribution:EUR 6 799 947
Coordinated in:United Kingdom
Call for proposal:FP7-ICT-2007-1
Funding scheme:CP - Collaborative project (generic)
Building robots capable to understand their own abilities and knowledge, and to choose which of their abilities or knowledge to extend at any timeCogX project builds what is missing for the development of cognitive systems able to work in open ended, challenging environments, dealing with novelty, uncertainty and change. Namely, how agents such as robots should understand their own abilities and knowledge (self-understanding), and choose which of their abilities or knowledge to extend at any one time (self-extension).
The high level aim of CogX is to develop a unified theory of self-understanding and self-extension with a convincing instantiation and implementation of this theory in a robot. This will require specific advances in the areas of mapping, language, and manipulation.
The challenge laid out in this call is to understand the principles according to which cognitive\nsystems should be built if they are to handle novelty, situations unforeseen by their designers, and\nopen-ended, challenging environments with uncertainty and change. Our aim is to meet this challenge by creating a theory -- evaluated in robots -- of how a cognitive system can model its own knowledge; use this to cope with uncertainty and novelty during task execution; extend its own abilities and knowledge; and extend its own understanding of those abilities. Imagine a cognitive system that models not only the environment, but its own understanding of the environment and how this understanding changes under action. It identifies gaps in its own understanding and then plans how to fill those gaps so as to deal with novelty and uncertainty in task execution, gather information necessary to complete its tasks, and to extend its abilities and knowledge so as to perform future tasks more efficiently.
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