Talal Rahwan

Talal Rahwan


Dr. Talal Rahwan is an assistant professor at Masdar Institute. He received the British Computer Society’s Distinguished Dissertation Award, which is annually presented to the UK’s best PhD thesis in the field of computer science. Dr. Rahwan was selected by the Institute of Electrical and Electronics Engineers Computer Society as one of the top 10 young Artificial Intelligence researchers in the world. He was awarded the Dean’s Award for “Early Career Researcher” at Southampton University’s Faculty of Physical Sciences and Engineering, UK.

Dr. Rahwan’s research interests include: Artificial Intelligence, Multi-Agent Systems, Cooperative Game Theory, and Combinatorial Optimization.

Multi-agent systems are an important and rapidly-growing design paradigm for understanding and building distributed systems. This paradigm is based on modeling the computational components as "agents"---autonomous entities (such as software programs or robots) that can decide for themselves what they need to do in order to satisfy their objectives. Typically, coexisting agents need to interact with one another to improve their performance and compensate for each other's deficiencies. One of the fundamental types of interactions is "Coalition Formation", which involves the creation of coalitions (i.e., teams) to optimize certain functions. Dr. Rahwan studies the computational aspects of coalition formation and is interested in developing algorithms to facilitate the coalition formation process.

Dr. Talal Rahwan received his Bachelor of Engineering in Informatics from the University of Aleppo, Syria (2003), and his Ph.D. from the School of Electronics and Computer Science (ECS), University of Southampton, UK, (2007).

Advisor to current Masdar Institute students

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Research directions Dr. Rahwan plans to work on in the future:

  • Develop Scalable Algorithms: Perhaps the biggest challenge when applying coalition formation technology in the real world is scalability. This is simply due to the inherent combinatorial nature of the space of possible solutions. For problems that are already hard to solve for tens of agents, coming up with solutions for thousands of agents requires the adoption of fundamentally different approaches and concepts.
  • Focus on energy domain: Collaboration has begun with researchers from King Abdul-Aziz University, Saudi Arabia, to develop a "peak-shaving" algorithm, whereby homes in a neighborhood form coalitions to coordinate the times at which each air condition is switched on/off. The goal of this coordination is to maintain each individual's level of comfort, while at the same time reducing the peak of the aggregate energy demand of the neighborhood, thus resulting in cheaper bills, and a greener system.
  • Identify Applications: Seeking inspiration from real-world domains to influence this research direction, Dr. Rahwan is keen on collaborating with researchers whose work is application-driven.


  • T. Rahwan, T. Michalak, M. Wooldridge, and N. R. Jennings (2012). Anytime Coalition Structure Generation in Multi-Agent Systems with Positive or Negative Externalities. Artificial Intelligent Journal (AIJ). 186, Pages 95-122.
  • T. Rahwan, S. D. Ramchurn, A. Giovannucci, and N. R. Jennings (2009). An Anytime Algorithm for Optimal Coalition Structure Generation. Journal of Artificial Intelligence Research (JAIR). 34, Pages 521-567.
  • T. Rahwan and N. R. Jennings (2007). An Algorithm for Distributing Coalitional Value Calculations among Cooperating Agents. Artificial Intelligence Journal (AIJ) 171(8-9). Pages 535-567.