Abstract
Communication among adaptive agents can be framed as language acquisition and broken down into three problems; symbol grounding, language learning, and language evolution. We propose that this view clarifies many of the difficulties framing issues of collaboration and self-organization. Additionally, we demonstrate simple classification systems that can provide the first step in grounding real-world data and provide general schema for constructing other such systems. The first system classifies auditory input from frog calls and is presented as a model of grounding objects. The second system uses the minimum description length framework to distinguish patterns of robot movement as a model of grounding actions.
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This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003
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Lee, Y., Collier, T., Taylor, C. et al. Adaptive communication among collaborative agents: preliminary results with symbol grounding. Artif Life Robotics 8, 127–132 (2004). https://doi.org/10.1007/s10015-004-0299-3
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DOI: https://doi.org/10.1007/s10015-004-0299-3