Population and Agent Based Models for Language Convergence

Kiran Lakkaraju, Les Gasser

Adaptive agents cooperating in rich, open environments will need shared ontologies and linguistic conventions to communicate critical information. In the face of open systems and environmental change, pre-defined ontologies are sub-optimal. In open systems, new agents or web services can require new conventions not captured in existing conceptualizations. A more adaptive and more theoretically interesting approach is to have agents negotiate repertoires of categories, meanings, and linguistic forms among themselves. Many related issues arise in the general arena of "emergent semantics"; the case of language serves as an excellent "model organism" for studying the issues, and results generalize nicely to other domains including "collaborative tagging" and bioinformatics. In this paper we look at two approaches to the "language convergence" problem, the population based approach and the agent based approach. We are investigating a middle ground between these two approaches that will incorporate the generalizability and tractability of population based models with the fine-grained control that Multi-Agent Systems based models provide.

Subjects: 7.1 Multi-Agent Systems; 7. Distributed AI

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