Learning a common language through an emergent interaction topology

Samarth Swarup, Kiran Lakkaraju, Les Gasser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the effects of various emergent topologies of interaction on the rate of language convergence in a population of communicating agents. The agents generate, parse, and learn sentences from each other using recurrent neural networks. An agent chooses another agent to learn from, based on that agent's fitness. Fitness is defined to include a frequency-dependent term capturing the approximate number of interactions an agent has had with others - its "popularity" as a teacher. This method of frequency-dependent selection is based on our earlier Noisy Preferential Attachment algorithm, which has been shown to produce various network topologies, including scale-free and small-world networks. We show that convergence occurs much more quickly with this strategy than it does for uniformly random interactions. In addition, this strategy more closely represents choice preference dynamics in large natural populations, and so may be more realistic as a model for adaptive language.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
Pages1381-1383
Number of pages3
DOIs
StatePublished - 2006
Externally publishedYes
EventFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS - Hakodate, Japan
Duration: May 8 2006May 12 2006

Publication series

NameProceedings of the International Conference on Autonomous Agents
Volume2006

Other

OtherFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Country/TerritoryJapan
CityHakodate
Period5/8/065/12/06

Keywords

  • Language evolution
  • Preferential attachment
  • Scale free networks

ASJC Scopus subject areas

  • Engineering(all)

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