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.

This paper describes a framework for studies of the adaptive acquisition and evolution of language, with the following components: language learning begins by associating words with cognitively salient representations (“grounding”); the sentences of each language are determined by properties of lexical items, and so only these need to be transmitted by learning; the learnable languages allow multiple agreements, multiple crossing agreements, and reduplication, as mildly context sensitive and human languages do; infinitely many different languages are learnable; many of the learnable languages include infinitely many sentences; in each language, inferential processes can be defined over succinct representations of the derivations themselves; the languages can be extended by innovative responses to communicative demands. Preliminary analytic results and a robotic implementation are described.