Artificial neural networks, ever since they regained interest from the mid-1980s on, have become a much discussed topic in artificial intelligence (AI). This in turn brought with it much debate as to the real contribution of neural networks to explaining and modelling cognition. This edited collection, brings together a selection of papers from experts in their field, outlining the concrete contribution that neural computing has made to AI. The result is a collection of arguments, examples and elaborations about how neural networks can not only contribute to a better AI, but somewhat revolutionize it by forming the basis for a truly alternative paradigm.
Artificial neural networks, ever since they regained interest from the mid-1980s on, have become a much discussed topic in artificial intelligence (AI). This in turn brought with it much debate as to the real contribution of neural networks to explaining and modelling cognition. This edited collection, brings together a selection of papers from experts in their field, outlining the concrete contribution that neural computing has made to AI. The result is a collection of arguments, examples and elaborations about how neural networks can not only contribute to a better AI, but somewhat revolutionize it by forming the basis for a truly alternative paradigm.
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ISBN-13
978-1-85032-172-9 (9781850321729)
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Schweitzer Klassifikation
Part I: General Topics - new AI as a whole. Eric Prem: Old and New AI; Chris Thornton: Representational Eclectism. Part II: Concrete approaches and research strategies. Hugues Bersini: The animal movement. Rolf Pfeifer & Paul Vershure: Distributed Adaptive Control. Georg Dorffner. Radical Connectionism. Paul Verschure: Connectionist explanation. Jari Vaario & Setsuo Ohsuga: Evolving Intelligence. Part III: Issues in modelling high-level cognition. Lars Niklasson & Noel Sharkey: Compositional connectionist representation & systematicity. Stefan Wermter & Ruth Hannuschka: A Connectionist model for metaphor. Joachim Diederich & James Hogan: Recruitment Learning. Max Garzon: Issues of computability.