The Robotics, Intelligent Systems, and Control (RISC) lab at KAUST is developing swarm robotics, enabling robots to work together on collaborative tasks with limited human supervision. RISC is using game theory to improve how robots make coordinated decisions in scenarios like engaging intruders or tracking oil spills. The lab is also researching programmable self-assembly for robot swarms. Why it matters: This research advances autonomous multi-agent systems for critical applications like search and rescue and environmental monitoring in the region.
This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.
Munther Dahleh from MIT gave a talk on information design under uncertainty, focusing on the challenges of creating an information marketplace. The talk addressed the externality faced by firms when information is allocated to competitors, and considered two models for this externality. The presentation included mechanisms for both models and highlighted the impact of competition on the revenue collected by the seller. Why it matters: The research advances understanding of information markets and mechanism design, relevant to the growing data economy in the GCC region.
This paper addresses exploration in reinforcement learning (RL) in unknown environments with sparse rewards, focusing on maximum entropy exploration. It introduces a game-theoretic algorithm for visitation entropy maximization with improved sample complexity of O(H^3S^2A/ε^2). For trajectory entropy, the paper presents an algorithm with O(poly(S, A, H)/ε) complexity, showing the statistical advantage of regularized MDPs for exploration. Why it matters: The research offers new techniques to reduce the sample complexity of RL, potentially enhancing the efficiency of AI agents in complex environments.
This paper introduces a decentralized multi-agent decision-making framework for search and action problems under time constraints, treating time as a budgeted resource where actions have costs and rewards. The approach uses probabilistic reasoning to optimize decisions, maximizing reward within the given time. Evaluated in a simulated search, pick, and place scenario inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), the algorithm outperformed benchmark strategies. Why it matters: The framework's validation in a Gazebo environment signals potential for real-world robotic applications, particularly in time-sensitive and cooperative tasks within the robotics domain in the UAE.
Abu Dhabi's Technology Innovation Institute (TII) has developed a new quantum optimization solver in collaboration with NVIDIA, Los Alamos National Laboratory, and Caltech. The solver addresses large-scale combinatorial optimization problems using a small number of qubits, encoding over 7000 variables with only 17 qubits. Published in Nature Communications, the research demonstrates a hybrid quantum-classical algorithm with a novel encoding scheme that maximizes the use of quantum resources. Why it matters: This advancement marks a significant step toward practical quantum computing applications in the UAE and beyond, particularly in solving complex optimization challenges across various sectors.
Conor McMenamin from Universitat Pompeu Fabra presented a seminar on State Machine Replication (SMR) without honest participants. The talk covered the limitations of current SMR protocols and introduced the ByRa model, a framework for player characterization free of honest participants. He then described FAIRSICAL, a sandbox SMR protocol, and discussed how the ideas could be extended to real-world protocols, with a focus on blockchains and cryptocurrencies. Why it matters: This research on SMR protocols and their incentive compatibility could lead to more robust and secure blockchain technologies in the region.