Research Interests

My research interests are in Artificial Intelligence. In particular I am interested in the following topics:

1.      Anomaly detection and diagnosis:
Anomaly detection procedure is necessary in many systems: physical systems, software, autonomous systems, networks, cyber systems, etc. Once an anomaly has been detected a further diagnosis task is essential to identify the root cause of the anomaly. The diagnosis enables a decision support system to recommend either repairing a certain component or replacing it or even tolerate the fault.
In my research I am interested mainly in Model-Based Diagnosis (MBD) techniques, but also data driven techniques. MBD relies on a model of the diagnosed system, which is utilized to simulate the behavior of the system given the inputs. The outputs are compared to the actual behavior to detect discrepancies indicating failures. The model can then be used to pinpoint possible failing components within the system. To address diagnosis problems, I established the Fault Detection and Diagnosis Lab at BGU, which promotes research with the government and leading corporations such as General Motors, Mekorot and IBM.. For example, diagnosis of faulty communication components in networks, identifying faulty components in distributed systems, or diagnose faults in UAVs.  In addition, I am interested in using MBD for new theoretical problems, such as using SAT solvers for MBD, the differences between intermittent and non-intermittent faults and the influence of different parameters of the systems on MBD algorithms.

In the last years I started to work on cyber security research. Cyber security tries to identify anomalous events, users communication etc. This is related to my background of trying to identify faults and to reason about their root cause. In this research I use especially machine learning techniques. For example, anomaly detection using temporal pattern recognition for SCADA systems, anomalous user detection by user profiling, and threat sharing models and representations.

My new research using machine learning techniques on the one hand, and my deep background in model-based diagnosis on the other hand, brought me to the conclusion that the combination of these two techniques is a key factor for efficient and strong new tools for the diagnosis problem. Following this conclusion, in the last years, I started to work on this hybrid method. I used it in the automated debugging area and now I implement it in some new projects dealing with faults in water main systems, rolling systems, and cyber-physical systems.

 

2.     Voting procedures to minimize preference elicitation:
Voting is an essential decision-making mechanism in multi-agent systems (MAS) that allows multiple agents to rank possible candidates and chooses a winner that reflects their joint preferences.
Previous work has typically assumed that the voters provide a complete set of preferences to the center. However, in practice, especially in distributed settings, it may not be feasible for all agents to submit a complete set of preferences. First, in real-world applications, it may be impractical to expect individuals to provide all their preferences for a large number of candidates, from the perspective of the human interface and due to the need to interrupt the human as less as possible. For example, if a group of friends wish to choose a movie to watch together, it would be impractical to ask all the members to individually rate all current movies, i.e., the candidate set.
A second motivating scenario for submitting an incomplete set of preferences requirement may be seen in telecommunications. In this domain, a basic requirement is to save on communications in order to reduce bandwidth overhead. For example, consider a meeting schedule involving prospective participants using their PDAs to vote on meeting times. A user would not specify preferences for all possible slots since there are hundreds a month. Furthermore, it would be costly to communicate all preferences to the center conducting the vote.
In practice, it is possible to determine a winner by specifically requesting agents for certain preferences rather than for their whole set of preferences. A key question is what partial information is essential for determining a winner? In my research I address this question


3. Decision making under uncertainty:
How to make decisions when faced with dynamically changing information is an important problem. Do you stop at a particular point and make the best decision you can, given the information you have so far, or do you wait until more information arrives so you can make a better decision?
When there is a cost to waiting, this problem becomes nontrivial. As an example, consider a meeting scheduling system. Determining the best time for a meeting could depend on many factors like other meetings, location and attendees. Typically, these factors may change dynamically. The longer one waits, the more information becomes available, and the higher the probability of choosing the best time. However, waiting to make the decision could be associated with a cost, for instance because the chosen time slot might no longer be available.
My research addresses two major challenges associated with decision making with dynamically arriving information:
(1) Development of models of dynamic information and how they affect the utility of the candidates.
(2) Development of methods to make a decision and pick the best candidate when the information is accumulating dynamically.