A new framework for sensor interpretation: planning to resolve sources of uncertainty

  • Authors:

  • Norman Carver;Victor Lesser

  • Affiliations:

  • Department of Computer and Information Science, University of Massachusetts, Amherst, Massachusetts;Department of Computer and Information Science, University of Massachusetts, Amherst, Massachusetts

  • Venue:
  • AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
  • Year:
  • 1991

Quantified Score

Hi-index 0.00

Visualization

Abstract

Sensor interpret.ation involves the determination of high-level explalliations of sensor data. Blackboard-based interpretation systems have usually been limited to incremental hypothesize and test strategies for resolving uncertainty. We have developed a new interpretation framework that supports the use of more sophisticated strat1egies like differential diagnosis. The RESUN framework has two key components: an evidential represent,ation that includes explicit, symbolic encodings of the sources of uncertainty (SOUs) in the evidence for hypotheses and a script-based, incremental control planner. Interpretation is viewed as an incremental process of gathering evidence to resolve particular sources of uncertainty. Control plans invoke actions that examine the symbolic SOUs associated with hypotheses and use the resulting information to post goals to resolve uncertadnty. These goals direct the system to expand methods appropriate for resolving the current sources of uncertlLinty in the hypotheses. The planner's refocusing mechanism makes it possible to postpone focusing decisions when there is insufficient information to make decisions and provides opportunistic control capabilities. The RESUN framework has been implemented and experimentally verified using a simulated aircraft monitorilllg application.