An Incremental Model of Lexicon Consensus in a Population of Agents by Means of Grammatical Evolution, Reinforcement Learning and Semantic Rules

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@InProceedings{Mingo:2011:IWINAC,

• author = "Jack Mario Mingo and Ricardo Aler",
• title = "An Incremental Model of Lexicon Consensus in a Population of Agents by Means of Grammatical Evolution, Reinforcement Learning and Semantic Rules",
• year = "2011",
• booktitle = "4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I",
• editor = "Jose Ferrandez and Jose Alvarez Sanchez and Felix de la Paz and F. Toledo",
• volume = "6686",
• series = "Lecture Notes in Computer Science",
• pages = "40--49",
• address = "La Palma, Canary Islands, Spain",
• month = may # " 30 - " # jun # " 3",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Swarm Intelligence, Language Acquisition and Language Development",
• DOI = "doi:10.1007/978-3-642-21344-1_5",
• size = "10 pages",
• abstract = "We present an incremental model of lexicon consensus in a population of simulated agents. The emergent lexicon is evolved with a hybrid algorithm which is based on grammatical evolution with semantic rules and reinforcement learning. The incremental model allows to add subsequently new agents and objects to the environment when a consensual language has emerged for a steady set of agents and objects. The main goal in the proposed system is to test whether the emergent lexicon can be maintained during the execution when new agents and object are added. The proposed system is completely based on grammars and the results achieved in the experiments show how building a language starting from a grammar can be a promising method in order to develop artificial languages.",
• affiliation = "Computer Science Department, Autonomous University of Madrid, Spain",

}

Genetic Programming entries for Jack Mario Mingo Ricardo Aler Mur

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