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

Ben Goertzel

ISBN 10: 9491216104 / ISBN 13: 9789491216107

Editore: Atlantis Press Dez 2011, 2011

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 280 pp. Englisch.

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Dati bibliografici

• Titolo: Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference
• Editore: Atlantis Press Dez 2011
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 9491216104
• ISBN 13: 9789491216107
• Rilegatura: Buch
• Condizione: Neu

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Riassunto

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.

Contenuti

Introduction.- Knowledge Representation Using Formal Logic.- Quantifying and Managing Uncertainty.- Representing Temporal Knowledge.- Temporal Reasoning.- Representing and Reasoning On Spatial Knowledge.- Representing and Reasoning on Contextual Knowledge.- Causal Reasoning.- Extracting Logical Knowledge from Raw Data.- Scalable Spatiotemporal Logical Knowledge Storage.- Mining Patterns from Large Spatiotemporal Logical Knowledge Stores.- Probabilistic Logic Networks.- Temporal and Contextual Reasoning in PLN.- Inferring the Causes of Observed Changes.-Adaptive Inference Control.