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Proceedings Paper

Planning under uncertainty in the NASA FTS environment
Author(s): Michael C. Moed; Robert B. Kelley
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Paper Abstract

An evaluation system called the associative rule memory (ARM) that operates with an interactive or automatic planner in a robot-based world, such as the world of the NASA Flight Telerobotic Servicer (FTS), is described. The ARM is constructed from a neural network model called a Boltzmann Machine, and ranks alternative robotic actions based on the probability that the action works as expected in achieving a desired effect. The system is experience-based, and can predict the probability of achieving a desired effect for robotic actions that have not been explicitly tested in the past. The ARM is designed to quickly and efficiently find high probability of effect for robotic actions for a given desired effect. This paper details the construction of the ARM for the NASA FTS robotic environment. Examples are also provided that demonstrate the use of the ARM within a current NASA symbolic planning system.

Paper Details

Date Published: 1 March 1992
PDF: 11 pages
Proc. SPIE 1612, Cooperative Intelligent Robotics in Space II, (1 March 1992); doi: 10.1117/12.56748
Show Author Affiliations
Michael C. Moed, United Parcel Service (United States)
Robert B. Kelley, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 1612:
Cooperative Intelligent Robotics in Space II
William E. Stoney, Editor(s)

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