Recognizing geospatial patterns with biologically-inspired relational reasoning

Paul Kogut, June Gordon, David Morgenthaler, John E Hummel, Edward Monroe, Ben Goertzel, Ethan Trewhitt, Elizabeth Whitaker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Relational reasoning is a complex high-level cognitive function that should be part of a realistic computational equivalent of the human mind. People use relational reasoning often in everyday life in many different contexts (e.g., social understanding, political science, law, business). This paper discusses the application of relational reasoning to the recognition of geospatial patterns (e.g., clusters of buildings that constitute a facility). The relational reasoning model is based on cognitive science evidence and emerging neuroscience theory. Experiments show that the relational reasoning model can recognize geospatial patterns that have a significant degree of variation.

Original languageEnglish (US)
Title of host publicationBiologically Inspired Cognitive Architectures 2011 Proceedings of the Second Annual Meeting of the BICA Society
PublisherIOS Press
Pages203-208
Number of pages6
ISBN (Print)9781607509585
DOIs
StatePublished - 2011

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume233
ISSN (Print)0922-6389

Keywords

  • Relational reasoning
  • analogy
  • geospatial

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Recognizing geospatial patterns with biologically-inspired relational reasoning'. Together they form a unique fingerprint.

  • Cite this

    Kogut, P., Gordon, J., Morgenthaler, D., Hummel, J. E., Monroe, E., Goertzel, B., Trewhitt, E., & Whitaker, E. (2011). Recognizing geospatial patterns with biologically-inspired relational reasoning. In Biologically Inspired Cognitive Architectures 2011 Proceedings of the Second Annual Meeting of the BICA Society (pp. 203-208). (Frontiers in Artificial Intelligence and Applications; Vol. 233). IOS Press. https://doi.org/10.3233/978-1-60750-959-2-203