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Toward Self-Referential Autonomous Learning of Object and Situation Models

Damerow, Florian ; Knoblauch, Andreas ; Körner, Ursula ; Eggert, Julian ; Körner, Edgar (2022)
Toward Self-Referential Autonomous Learning of Object and Situation Models.
In: Cognitive Computation, 2016, 8 (4)
doi: 10.26083/tuprints-00020319
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Toward Self-Referential Autonomous Learning of Object and Situation Models
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2016
Publisher: Springer Nature
Journal or Publication Title: Cognitive Computation
Volume of the journal: 8
Issue Number: 4
DOI: 10.26083/tuprints-00020319
Corresponding Links:
Origin: Secondary publication service

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.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-203190
Additional Information:

Keywords: Self-referential control, Scene understanding, Autonomous learning, Hierarchical situation model

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 14 Jan 2022 08:12
Last Modified: 21 Mar 2023 10:34
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20319
PPN: 506163326
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