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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. (Publisher's Version)
In: Cognitive Computation, 8 (4), pp. 703-719. Springer Nature, ISSN 1866-9956, e-ISSN 1866-9964,
DOI: 10.26083/tuprints-00020319,
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Item Type: Article
Origin: Secondary publication service
Status: Publisher's Version
Title: Toward Self-Referential Autonomous Learning of Object and Situation Models
Language: English
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.

Journal or Publication Title: Cognitive Computation
Volume of the journal: 8
Issue Number: 4
Publisher: Springer Nature
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
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: 14 Jan 2022 08:13
DOI: 10.26083/tuprints-00020319
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-203190
Additional Information:

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

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20319
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