• Corpus ID: 223953384

Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces

@article{Burgess2020TestingTQ,
  title={Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces},
  author={Mark Burgess},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.08125}
}
  • M. Burgess
  • Published 23 September 2020
  • Computer Science
  • ArXiv
Given a pool of observations selected from a sensor stream, input data can be robustly represented, via a multiscale process, in terms of invariant concepts, and themes. Applying this to episodic natural language data, one may obtain a graph geometry associated with the decomposition, which is a direct encoding of spacetime relationships for the events. This study contributes to an ongoing application of the Semantic Spacetime Hypothesis, and demonstrates the unsupervised analysis of narrative… 

References

SHOWING 1-10 OF 65 REFERENCES
Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape
TLDR
This work studies the validity of the Semantic Spacetime Hypothesis, for the extraction of concepts as process invariants, and suggests that what the authors consider important and interesting about sensory experience is not solely based on higher reasoning, but on simple spacetime process cues, and may be how cognitive processing is bootstrapped in the beginning.
Spacetimes with Semantics (III) - The Structure of Functional Knowledge Representation and Artificial Reasoning
TLDR
Using the previously developed concepts of semantic spacetime, this work explores the interpretation of knowledge representations, and their structure, as a semantic system, within the framework of promise theory, with a focus on concepts, associative knowledge, and context awareness.
A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning
TLDR
A quasi-linguistic approach to knowledge representation is discussed, motivated by spacetime structure, which is then parsed with very simple recursive algorithms to generate `brainstorming' sets of reasoned knowledge.
Spacetimes with Semantics
TLDR
The aim of this exercise is to apply related tools and ideas to an initial unification of real and artificial spaces, e.g. databases and information webs with natural spacetime, by reconstructing these spaces from autonomous agents, to better understand naming and coordinatization of semantic spaces.
Cognitive Grammar: A Basic Introduction
TLDR
This book presents a synthesis that draws together and refines the descriptive and theoretical notions developed in this framework over the course of three decades in a unified manner that accomodates both the conceptual and the social-interactive basis of linguistic structure.
Interacting Conceptual Spaces I : Grammatical Composition of Concepts
TLDR
This work introduces the category of convex relations as a new setting for categorical compositional semantics, emphasizing the convex structure important to conceptual space applications, and shows how to construct conceptual spaces for various types such as nouns, adjectives and verbs.
Spacetimes with Semantics (II)
Using Promise Theory as a calculus, I review how to define agency in a scalable way, for the purpose of understanding semantic spacetimes. By following simple scaling rules, replacing individual
COGNITIVE GRAMMAR
  • A. Tyler
  • Linguistics
    Studies in Second Language Acquisition
  • 2005
COGNITIVE GRAMMAR. John R. Taylor. Oxford: Oxford University Press, 2002. Pp. xii + 621. $29.95 paper. This volume is not aimed at SLA researchers but is a book that SLA researchers interested in
Probabilistic reasoning in intelligent systems - networks of plausible inference
  • J. Pearl
  • Computer Science
    Morgan Kaufmann series in representation and reasoning
  • 1989
TLDR
The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.
Consciousness as a state of matter
...
...