Storing and Predicting Dynamic Attributes in a World Model Knowledge Store

Abstract

The world is an ever-changing, dynamic environment. If robots and other intelligent systems are to find ways to cope with and reason about the world adequately, they must be capable of understanding these dynamic features. This dissertation examines the need for a centralized knowledge store capable of storing information that is both spatial and temporal in nature. The interface of a new and unique architecture to handle the exchange of dynamic information and questions about the future state of that information is presented. A novel algorithm, called the Statistics-Based Nth Order Polynomial Predictor (SNOPP), is also developed which allows state prediction of almost any time-variant data.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2009
Accession Number
ADA501148

Entities

People

  • Daniel A. Kent

Organizations

  • University of Florida

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Autonomous Navigation
  • Autonomous Systems
  • Autonomous Vehicles
  • Computer Programming
  • Computers
  • Coordinate Systems
  • Database Management Systems
  • Databases
  • Information Science
  • Motion Planning
  • Operating Systems
  • Relational Database Management Systems
  • Robots
  • Simultaneous Localization And Mapping
  • Unmanned Systems
  • Unmanned Vehicles

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy