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

  • Book
  • © 2011

Overview

Authors:
  1. Ben Goertzel

    1. Novamente LLC, Rockville, USA

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  2. Nil Geisweiller

    1. Rockville, USA

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  3. Lucio Coelho

    1. Belo Horizonte, Brazil

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  4. Predrag Janičić

    1. Faculty of Mathematics, Belgrade, Serbia

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  5. Cassio Pennachin

    1. Novamente LLC/Biomind LLC, Rockville, USA

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  • The book gives the first accessible, reasonably comprehensive and unified review of the various existing approaches to any of spatial, temporal or contextual logic
  • The book breaks new research ground via explaining theoretically and by means of examples how uncertain logical inference can be applied in the context of real-world examples of spatial, temporal and contextual logic
  • Includes supplementary material: sn.pub/extras

Part of the book series: Atlantis Thinking Machines (ATLANTISTM, volume 2)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
    Pages 11-24

  3. Representations and Rules for Real-World Reasoning

    1. Front Matter

      Pages 25-25
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    2. Knowledge Representation Using Formal Logic

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 27-51

    3. Quantifying and Managing Uncertainty

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 53-64

    4. Representing Temporal Knowledge

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 65-78

    5. Temporal Reasoning

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 79-97

    6. Representing and Reasoning On Spatial Knowledge

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 99-122

    7. Representing and Reasoning on Contextual Knowledge

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 123-145

    8. Causal Reasoning

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 147-157

  4. Acquiring, Storing and Mining Logical Knowledge

    1. Front Matter

      Pages 159-159
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    2. Extracting Logical Knowledge from Raw Data

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 161-167

    3. Scalable Spatiotemporal Logical Knowledge Storage

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 169-177

    4. Mining Patterns from Large Spatiotemporal Logical Knowledge Stores

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 179-185

  5. Probabilistic Logic Networks for Real-World Reasoning

    1. Front Matter

      Pages 187-187
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    2. Probabilistic Logic Networks

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 189-204

    3. Temporal and Contextual Reasoning in PLN

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 205-220

    4. Inferring the Causes of Observed Changes

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 221-245

    5. Adaptive Inference Control

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 247-263

    6. Conclusion

      • Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, Cassio Pennachin
      Pages 265-266
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Keywords

About this book

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.

Authors and Affiliations

  • Novamente LLC, Rockville, USA

    Ben Goertzel

  • Rockville, USA

    Nil Geisweiller

  • Belo Horizonte, Brazil

    Lucio Coelho

  • Faculty of Mathematics, Belgrade, Serbia

    Predrag Janičić

  • Novamente LLC/Biomind LLC, Rockville, USA

    Cassio Pennachin

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