| 書目名稱 | From Synapses to Rules | | 副標題 | Discovering Symbolic | | 編輯 | Bruno Apolloni,Franz Kurfess | | 視頻video | http://file.papertrans.cn/349/348967/348967.mp4 | | 圖書封面 |  | | 描述 | One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. of being tested, manipulated and composed with These must be susceptible other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in t | | 出版日期 | Book 2002 | | 關(guān)鍵詞 | Nervous System; algorithms; artificial intelligence; fuzzy; intelligence; knowledge; learning; modeling; neu | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-0705-5 | | isbn_softcover | 978-1-4613-5204-4 | | isbn_ebook | 978-1-4615-0705-5 | | copyright | Springer Science+Business Media New York 2002 |
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