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| Content Provider | Springer Nature Link |
|---|---|
| Author | Damerow, Florian Knoblauch, Andreas Körner, Ursula Eggert, Julian Körner, Edgar |
| Copyright Year | 2016 |
| Abstract | Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach. |
| Starting Page | 703 |
| Ending Page | 719 |
| Page Count | 17 |
| File Format | |
| ISSN | 18669956 |
| Journal | Cognitive Computation |
| Volume Number | 8 |
| Issue Number | 4 |
| e-ISSN | 18669964 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2016-04-27 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Self-referential control Scene understanding Autonomous learning Hierarchical situation model Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
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