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

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

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Atlantis Thinking Machines

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

Ben Goertzel | Nil Geisweiller | Lucio Coelho | Predrag Janičić | Cassio Pennachin

Computers / General

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.

Publication Date: 07 December 2011
Publisher: Atlantis Press
Imprint: Atlantis Press
ISBN-13: 9789491216107
Format: Hardback
Page Count: 269

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