On Machine Symbol Grounding and Optimization

Oliver Kramer
2011 International Journal of Cognitive Informatics and Natural Intelligence  
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
more » ... und 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.
doi:10.4018/ijcini.2011070105 fatcat:qvlfoyfkfvdl7bmtxykq4zwtza