Schemata and sequential thought processes in PDP models
Parallel distributed processing
Constructive Backpropagation for Recurrent Networks
Neural Processing Letters
MyLifeBits: fulfilling the Memex vision
Proceedings of the tenth ACM international conference on Multimedia
Wearable Sensing to Annotate Meeting Recordings
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
On Intelligence
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A lifetime recording agent that suggests unusual events to a user is proposed. The goal is to create a memory device that supports human memory by filtering, categorizing, and remembering everyday events. In ubiquitous sensor environments, the agent classifies users’ experiences represented by surrounding objects and predicts typical events that a user will experience next. Unusual events are detected by the awareness of different characteristics as the human brain does. If the prediction is incorrect, the actual event is considered to be unusual. A recurrent neural network that autonomously alters its architecture is introduced to perform event prediction. Experiments confirm: (1) a suitable hierarchical level of event categories for a current situation can be obtained by estimating the event prediction performance, that is, the recall rate and (2) rehearsal sequences dynamically generated by the network can substitute for a sequence of actual events. Thus, the agent easily responds to new environments without forgetting previous memories.