@inproceedings{528583089e454b3aa75982f39ac84b05,
title = "A hybrid approach for fault detection in autonomous physical agents",
abstract = "One of the challenges of fault detection in the domain of autonomous physical agents (or Robots) is the handling of unclassified data, meaning, most data sets are not recognized as normal or faulty. This fact makes it very challenging to use collected data as a training set such that learning algorithms would produce a successful fault detection model. Traditionally unsupervised algorithms try to address this challenge. In this paper we present a hybrid approach that combines unsupervised and supervised methods. An unsupervised approach is utilized for classifying a training set, and then by a standard supervised algorithm we build a fault detection model that is much more accurate than the original unsupervised approach. We show promising results on simulated and real world domains.",
keywords = "Fault detection, Model-based diagnosis, Robotics, UAV",
author = "Eliahu Khalastchi and Meir Kalech and Lior Rokach",
note = "Publisher Copyright: Copyright {\textcopyright} 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 ; Conference date: 05-05-2014 Through 09-05-2014",
year = "2014",
month = jan,
day = "1",
language = "English",
series = "13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "941--948",
booktitle = "13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014",
}