Год издания: 2005
Количество страниц: 256
В продаже с 18.01.2012
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Bayesian nets are widely used in artificial intelligence as a calculus for casual reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover casual relationships. But many philosophers have criticized and ulimately rejected the central assumption on which such work is based-the causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence? This book argues that the Causal Markov Condition holds as default rule: it often holds but may need to be repealed in the face of counter examples. Thus, Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account of causal reasoing and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent. The resulting framework for causal reasoning involves not only new algorithms, but also new conceptual...