Oliver Kramer


Autonomous systems gather high-dimensional sensorimotor data with their multimodal sensors. Symbol grounding is about whether these systems can, based on this data, construct symbols that serve as a vehicle for higher symbol-oriented cognitive processes. Machine learning and data mining techniques are geared towards finding structures and input-output relations in this data by providing appropriate interface algorithms that translate raw data into symbols. Can autonomous systems learn how to ground symbols in an unsupervised way, only with a feedback on the level of higher objectives? A target-oriented optimization procedure is suggested as a solution to the symbol grounding problem. It is demonstrated that the machine learning perspective introduced in this paper is consistent with the philosophical perspective of constructivism. Interface optimization offers a generic way to ground symbols in machine learning. The optimization perspective is argued to be consistent with von Glasersfeld’s view of the world as a black box. A case study illustrates technical details of the machine symbol grounding approach.


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Paper Citation

in Harvard Style

Kramer O. (2011). MACHINE SYMBOL GROUNDING AND OPTIMIZATION . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 464-469. DOI: 10.5220/0003274304640469

in Bibtex Style

author={Oliver Kramer},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
SN - 978-989-8425-40-9
AU - Kramer O.
PY - 2011
SP - 464
EP - 469
DO - 10.5220/0003274304640469