526 publications from this institution
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.
Insurance riders are optional addendum to base insurance policies. In this paper we discuss the application of recommender systems to the task of matching riders to clients. This task is difficult because of the variety of possible riders, as well as the poor knowledge of the client over these riders. We focus on call centers where the agent also has limited knowledge and expertise. For such agents, discovering appropriate riders for the current client is very difficult, and automated tools that suggest such riders can play an important role in the agent-client dialogue, and may influence considerably the outcome of the interaction.
