game theory

Traffic Monitoring and MAC-Layer Design for Future IoT Systems

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B1 L4 R4214

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step.

Wireless Network Intelligence @ the Edge

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B1 L3 R3119

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

Towards Collaboration of Multiple Intelligent Robots and Human Supervision

Dr. Inmo Jang, Postdoctoral Researcher, Robotics for Extreme Environment Group at the University of Manchester

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B2 L5 R5209

As autonomy in individual robots becomes advanced, one of the next challenges is to coordinate multiple of such intelligent robots, which are then expected to innovatively transform legacy industries (e.g., warehouse automation, connected-vehicle management, etc.). Towards collaboration of multiple robots, this talk will particularly introduce a game-theoretical framework for clustering a large number of multiple robots and assigning the robot teams to given tasks, where the network of the robots is strongly connected and the individuals are asynchronous. The proposed decentralised algorithm guarantees convergence of selfish agents having social inhibition towards a Nash stable partition (i.e., social agreement) within polynomial time.

Game-Theoretic Analysis of Smart Grid Markets: From Retail to Wholesale

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KAUST

As end-consumers of electricity become more proactive and as many countries around the world push for a deeper penetration of renewable resources into the power grid, critical issues and challenges arise to the design and operation of deregulated electricity markets. In this presentation, we show how one can exploit tools from game theory to address some of these critical issues. Firstly, wholesale and retail markets are becoming more integrated due to the increasing adoption of distributed energy resources, creating a large gap in the current understanding of the impact of such small-scale energy resources on the larger power system operation and electricity market outcomes. This motivates us to develop a metric, called the Price of Aggregation, which quantifies the impact of integrating distributed energy resources in the retail-level on wholesale market efficiency. Secondly, evidence from real markets indicate that large-scale adoption of wind energy in the transmission system leads to significantly higher price volatility in wholesale markets. To mitigate the effects of price volatility, we propose an add-on centralized clearing mechanism that is applicable to any wholesale market, with the aim of allowing any market participant to hedge against profit volatilities, without changing the existing market operations. Finally, we develop a multiperiod-multicompany demand response framework in retail markets, which captures the behavior of competing companies and their price-responsive end-consumers. Using real-life data, we demonstrate potential savings that can exceed 30% for end-consumers, in addition to revealing desirable mathematical properties and deep insights.

Multi-Robot Task Allocation Games in Dynamically Changing Environments with Application to Trash Collection

Shinkyu Park, Assistant Professor, Electrical and Computer Engineering, KAUST

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KAUST

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Jeff Shamma elected IFAC fellow 2014-17

1 min read ·
The award is an international distinction given to scientists for their contributions in the fields of interest of IFAC under the form of technical publications, patents, control solutions, products, software, and leadership in research, development, and education.

Robotics, Intelligent Systems, and Control Lab prepares robots to have swarm intelligence

1 min read ·
The KAUST Robotics, Intelligent Systems, and Control (RISC) lab takes a "think globally, act locally" approach in swarm robotics. The RISC lab develops methodologies to deploy robots to work together to achieve collaborative tasks in missions involving crucial time-sensitive tasks, such as team search and rescue, patrolling for safety and environmental monitoring. To address these applications, the RISC lab develops algorithms to enable robots to operate with limited or even no human supervision. "We want to move away from the low-level remotely operated model to more of a high-level human

Yao GE

Visiting Scholar with the Department of Computer, Electrical and Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST)

Norah Alghamdi

Education 2021.07 - Present Ph.D. Student in Electrical and Computer Engineering, KAUST, KSA 2018.08 - 2021.06 M.S. in Electrical and Computer Engineering, KAUST, KSA 2013.08 - 2018.07 B.S. in Electrical and Computer Engineering, King Abdulaziz University, KSA Selected Publications N.K. Alghamdi, D.A. Shamma, J.S. Shamma, "Analyzing the Passivity of the Human Decision Making Rule in a Congestion Game Experiment" 2022 IEEE American Control Conference (ACC), 4826-4831, (2022). Areas of Expertise and Research Interests ​Game Theory Multi-robot Systems Robotics Human-robot Interaction

Enas M. Odat

Enas Odat received the B.S. degree in computer engineering from the Jordan University of Science and Technology in 2009, and the M.S. degree from the Department of Computer Science, King Abdullah University of Science and Technology, in 2011, where she is currently pursuing the Ph.D. degree. Her research interests include machine learning, game theory, and Cyber-Physical systems. Education Profile MS. Computer Sciences, King Abdullah University of Sciences and Technology (KAUST), 2011 BS. Computer Engineering, Jordan University of Science and Technology (JUST), 2009

Eder D. Baron Prada

Ph.D. student at KAUST, my research interests include game theory, optimization, and control of multi-agent systems.

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research