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

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Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference is written by Ben Goertzel and published by Springer Science & Business Media. It's available with International Standard Book Number or ISBN identification 9491216112 (ISBN 10) and 9789491216114 (ISBN 13).

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.