toward scalable, uncertain spatiotemporal, contextual and causal inference /
First Statement of Responsibility
Ben Goertzel [and others]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (279 pages)
SERIES
Series Title
Atlantis thinking machines,
Volume Designation
v. 2
ISSN of Series
1877-3273 ;
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references (pages 267-279)
CONTENTS NOTE
Text of Note
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
0
SUMMARY OR ABSTRACT
Text of Note
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