Efficient Pathfinding in Very Large Data Spaces

Abstract

This project created a corpus of large test problems relevant to the Intelligence Community (IC). These problems required bringing together only facts and rules from the unclassified OpenCyc knowledge base. Because many current theorem provers are unable to a reason with (or even load) an IC-sized knowledge base, six different levels of problems were created, each containing progressively larger theories. Dozens of Automatic Theorem Proving (ATP) researchers are now heavily engaged in attacking more and more of these six new TPTP problem sets. In addition to challenging the theorem-proving community, this project contained a series of experiments to assess and, where possible, improve the efficiency of Cyc s general inference engine. These experiments identified areas for immediate improvement, and approximately one full factor of 10 speedup was obtained just in the course of carrying them out and analyzing their results.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2007
Accession Number
ADA475387

Entities

People

  • Douglas B. Lenat
  • Keith Goolsbey
  • Kevin Knight
  • Pace Smith

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Communities
  • Computational Science
  • Computer Science
  • Efficiency
  • Governments
  • Inference Engines
  • Intelligence Community
  • Language
  • Machine Learning
  • Ontologies
  • Reinforcement Learning
  • Statistical Analysis

Readers

  • Computational Linguistics
  • Educational Psychology
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Space