CogprintsPerceptual grounding in robotsPaulVogtauthorThis paper reports on an experiment in which robotic agents are able to ground objects in their environment using low-level sensors. The reported experiment is part of a larger experiment, in which autonomous agents ground an adaptive language through self-organization. Grounding is achieved by the implementation of the hypothesis that meaning can be created using mechanisms like feature generation and self-organization. The experiments were carried out to investigate how agents may construct features in order to learn to discriminate objects from each other. Meaning is formed to give semantic value to the language, which is also created by the agents in the same experiments. From the experimental results we can conclude that the robots are able to ground meaning in this self-organizing manner. This paper focuses on the meaning creation and will only discuss the language formation very briefly. The paper sketches the tested hypothesis, the experimental set-up and experimental results.Dynamical SystemsMachine LearningRobotics1997Springer-VerlagConference Paper

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