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

Ben Goertzel

ISBN 10: 9491216104 / ISBN 13: 9789491216107

Verlag: Atlantis Press Dez 2011, 2011

Beschreibung

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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 280 pp. Englisch.

Bestandsnummer des Verkäufers 9789491216107

Bibliografische Details

• Titel: Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference
• Verlag: Atlantis Press Dez 2011
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 9491216104
• ISBN-13: 9789491216107
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 21 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 21 mm
• Gewicht: 588 Gramm

Über diesen Titel

Inhaltsangabe

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.