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Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference

Ben GoertzelNil GeisweillerLucio CoelhoPredrag JaničićCassio Pennachin

出版	Springer Science & Business Media, 2011-12-02
主題	Computers / GeneralComputers / Management Information SystemsComputers / Artificial Intelligence / GeneralComputers / Computer ScienceBusiness & Economics / Business Mathematics
ISBN	94912161129789491216114
URL	http://books.google.com.hk/books?id=g7UAIhnmJpsC&hl=&source=gbs_api
EBook	SAMPLE

註釋

The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.