Local prediction-learning in high-dimension space enable neural network to plan

The task of planning a sequence of actions, and dynamically adjusting the plan in dependence of unforeseen circumstances, remains challenging for artificial intelligence frameworks. The authors introduce a learning approach inspired by cognitive functions, that demonstrates high flexibility and generalization capability in planning tasks, suitable for on-chip learning.

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