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Abstract

We consider the problem of linguistic agents that communicate with each other about a shared world. We develop a formal notion of a language as a set of probabilistic associations between form (lexical or syntactic) and meaning (semantic) that has general applicability. Using this notion, we define a natural measure of the mutual intelligibility, F(L,L'), between two agents, one using the language L and the other using L'. We then proceed to investigate three important questions within this framework: (1) Given a language L, what language L' maximizes mutual intelligibility with L? We find surprisingly that L' need not be the same as L and we present algorithms for approximating L' arbitrarily well. (2) How can one learn to optimally communicate with a user of language L when L is unknown at the outset and the learner is allowed a finite number of linguistic interactions with the user of L? We describe possible algorithms and calculate explicit bounds on the number of interactions needed. (3) Consider a population of linguistic agents that learn from each other and evolve over time. Will the community converge to a shared language and what is the nature of such a language? We characterize the evolutionarily stable states of a population of linguistic agents in a game-theoretic setting. Our analysis has significance for a number of areas in natural and artificial communication where one studies the design, learning, and evolution of linguistic communication systems.

Keywords: Linguistic agents; Optimal communication; Language learning; Language evolution; Game theory; Multi-agent systems

@article{komarova04optimizing,
  author={N. L. Komarova and P. Niyogi},
  title={Optimizing the mutual intelligibility of linguistic agents in a shared world},
  journal={Artificial Intelligence},
  year={2004},
  month={April},
  volume={154},
  number={1-2},
  pages={1-42},
  doi={10.1016/j.artint.2003.08.005},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/komarova04optimizing.html},
  keywords={Linguistic agents; Optimal communication; Language learning; Language evolution; Game theory; Multi-agent systems}
}

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