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Computational approaches to linguist...

Wang, Jun.

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Computational approaches to linguistic consensus.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational approaches to linguistic consensus./
作者:	Wang, Jun.
面頁冊數:	106 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-02, Section: A, page: 0386.
Contained By:	Dissertation Abstracts International68-02A.
標題:	Language, Linguistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3250342

Computational approaches to linguistic consensus.
Wang, Jun.

Computational approaches to linguistic consensus. - 106 p.

Source: Dissertation Abstracts International, Volume: 68-02, Section: A, page: 0386.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.

The main question we ask is how a common language might come about in complex adaptive language systems comprising many agents. Our primary objective is to analyze and design complex language models so that a group of agents can converge on a common language from their initially different languages by using reinforcement learning, a minimal information approach. Towards this end, we present a game-based self-organizing language framework, and study three important cases of reaching linguistic consensus: word consensus, coherent communication, and grammar consensus.Subjects--Topical Terms:

1018079
Language, Linguistics.

Computational approaches to linguistic consensus.
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245 1 0 $a Computational approaches to linguistic consensus.
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500 $a Source: Dissertation Abstracts International, Volume: 68-02, Section: A, page: 0386.
500 $a Adviser: Les Gasser.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
520 $a The main question we ask is how a common language might come about in complex adaptive language systems comprising many agents. Our primary objective is to analyze and design complex language models so that a group of agents can converge on a common language from their initially different languages by using reinforcement learning, a minimal information approach. Towards this end, we present a game-based self-organizing language framework, and study three important cases of reaching linguistic consensus: word consensus, coherent communication, and grammar consensus.
520 $a The study of word consensus concerns how agents can converge to a common word to reliably express a single shared meaning, out of a number of different words. We have proposed a win-stay lose-shift learning model, and have shown by computer simulation and mathematical analysis the conditions under which the agents in the model can converge to a common word.
520 $a The study of coherent communication concerns how agents can converge on a communication system in which the word used by a sender to represent some meaning can be interpreted correctly by a receiver to extract the same meaning. We have proposed a minimum reinforcement learning model comprising two agents (a sender and a receiver), and have shown by computer simulation and mathematical analysis the conditions under which agents in the model can converge to a coherent communication system.
520 $a The study of grammar consensus concerns how agents can converge to a common grammar. In the converged state, the sentences generated by one agent using his grammar can be recognized by another agent using her grammar. We have proposed a mutual perceptron learning model in which grammars are modeled as Boolean functions that can be used to classify or recognize Boolean instances (sentences), and have shown by mathematical analysis the conditions under which agents in this model can converge to a common grammar (i.e., a common Boolean function).
520 $a This work has important implications for many kinds of distributed semantic systems, such as shared web ontologies, agent communication protocols, collaborative tagging, database schema integration, and biological networks.
590 $a School code: 0090.
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