Model-based monitoring and diagnosis of systems with software-extended behavior

  • Authors:

  • Tsoline Mikaelian;Brian C. Williams;Martin Sachenbacher

  • Affiliations:

  • Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA

  • Venue:
  • AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Model-based diagnosis has largely operated on hard-ware systems. However, in most complex systems today, hardware is augmented with software functions that influence the system's behavior. In this paper, hard-ware models are extended to include the behavior of associated embedded software, resulting in more comprehensive diagnoses. Prior work introduced probabilistic, hierarchical, constraint-based automata (PHCA) to allow the uniform and compact encoding of both hard-ware and software behavior. This paper focuses on PHCA-based monitoring and diagnosis to ensure the robustness of complex systems. We introduce a novel approach that frames diagnosis over a finite time horizon as a soft constraint optimization problem (COP), allowing us to leverage an extensive body of efficient solution methods for COPs. The solutions to the COP correspond to the most likely evolutions of the complex system. We demonstrate our approach on a vision-based rover navigation system, and models of the SPHERES and Earth Observing One spacecraft.