Optimizing the mutual intelligibility of linguistic agents in a shared world

被引:26
作者
Komarova, N
Niyogi, P [1 ]
机构
[1] Univ Chicago, Dept Comp Sci, Chicago, IL 60637 USA
[2] Inst Adv Study, Princeton, NJ 08540 USA
[3] Univ Leeds, Dept Appl Math, Leeds LS2 9JT, W Yorkshire, England
关键词
linguistic agents; optimal communication; language learning; language evolution; game theory; multi-agent systems;
D O I
10.1016/j.artint.2003.08.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2003 Published by Elsevier B.V.
引用
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页码:1 / 42
页数:42
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