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[BOOK] The Computational Nature of Language Learning and Evolution this is the draft of a book entitled 'The Computational Nature of Language Learning and Evolution' download here: http://people.cs.uchicago.edu/~niyogi/Book.html the author is a professor of university of chicago the author's homepage: http://people.cs.uchicago.edu/~niyogi/ (the following is copied form the author's homepage. The language evolution seems interesting) Language Acquisition This is the classic learning problem that humans solve --- they learn their native language. I have developed (jointly with R. C. Berwick) algorithms for the acquisition of syntax and analyzed the informational complexity of learning syntactic problems. However, syntax is only a small part of the language acquisition story --- the child receives continuous speech inputs. From this it has to uncover the phonetic inventory, the phonological rules, the lexicon and so on. I am currently examining ways in which this sort of information can be extracted with a focus on acquiring phonetic and phonological knowledge. Progress would lead to computers that can automatically learn language directly from the speech signal --- much as humans do. Language Evolution A twist to the whole language acquisition story is provided by the fact that if children truly attained the language of the parental generation perfectly, then languages would be transmitted perfectly from generation to generation with no change. This however is not true since we know that languages change with time. By considering a population of language learners and taking ensemble averages over the population, one can derive models of language change. Such models are the evolutionary consequences of language learning. This has developed into an extremely promising direction of research and suggests a computational framework within which various aspects of historical linguistics and language evolution can be studied --- something that was not possible before. In addition to the obvious applications to historical linguistics, there are strong algorithmic connections to genetic algorithms, artificial life, populations of interacting agents, computational economic agents and the like that I would like to explore further to shed light on the general theme of the interaction of learning with evolution.    Êղؠ  ·ÖÏí   ¶¥(0)

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