Pattern mining for general intelligence: The FISHGRAM algorithm for frequent and interesting subhypergraph mining

Jade O'Neill, Ben Goertzel, Shujing Ke, Ruiting Lian, Keyvan Sadeghi, Chi Keung Simon Shiu, Dingjie Wang, Gino Tu Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Fishgram, a novel algorithm for recognizing frequent or otherwise interesting sub-hypergraphs in large, heterogeneous hypergraphs, is presented. The algorithm's implementation the OpenCog integrative AGI framework is described, and concrete examples are given showing the patterns it recognizes in OpenCog's hypergraph knowledge store when the OpenCog system is used to control a virtual agent in a game world. It is argued that Fishgram is well suited to fill a critical niche in OpenCog and potentially other integrative AGI architectures: scalable recognition of relatively simple patterns in heterogeneous, potentially rapidly-changing data.
Original languageEnglish
Title of host publicationArtificial General Intelligence - 5th International Conference, AGI 2012, Proceedings
Pages189-198
Number of pages10
DOIs
Publication statusPublished - 26 Dec 2012
Event5th International Conference on Artificial General Intelligence, AGI 2012 - Oxford, United Kingdom
Duration: 8 Dec 201211 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7716 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Artificial General Intelligence, AGI 2012
CountryUnited Kingdom
CityOxford
Period8/12/1211/12/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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