Optimizing the mutual intelligibility of linguistic agents in a shared world

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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

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