Adaptive communication among collaborative agents: preliminary results with symbol grounding

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.