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1. Exploiting Trust for Resilient Hypothesis Testing withMalicious Robots

   https://doi.org/10.1109/TRO.2024.3415235  

   Cavorsi, Matthew; Akgün, Orhan Eren; Yemini, Michal; Goldsmith, Andrea J; Gil, Stephanie (January 2024, IEEE Transactions on Robotics)

   We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. For the Two Stage Approach, we assume that the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to simultaneously estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis. We exploit particular structures in the problem to show that this approach remains computationally tractable even with unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions subject to a Sybil attack on a mock-up road network. We extract the trust observations for each robot from communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT algorithms respectively. 

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2. Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots

   Cavorsi, M.; Akgün, O.E.; Yemini, M.; Goldsmith, A.; Gil, S. (September 2022, arXiv:2209.12285v1)

   We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit to Google Maps, subject to a Sybil attack. We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively. 

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3. Comparing Stochastic Optimization Methods for Multi-robot, Multi-target Tracking

   https://doi.org/10.1007/978-3-031-51497-5_27  

   Xin, Pujie; Dames, Philip (February 2024, Springer Nature Switzerland)

   This paper compares different distributed control approaches which enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have a limited field of view (FoV) and they are required to explore the environment. The team uses a distributed formulation of the Probability Hypothesis Density (PHD) filter to estimate the number and the position of the targets. The resulting target estimate is used to select the subsequent search locations for each robot. This paper compares Lloyd’s algorithm, a traditional method for distributed search, with two typical stochastic optimization methods: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). This paper presents novel formulations of PSO and SA to solve the multi-target tracking problem, which more effectively trade off between exploration and exploitation. Simulations demonstrate that the use of these stochastic optimization techniques improves coverage of the search space and reduces the error in the target estimates compared to the baseline approach. 

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4. Learning Trust Over Directed Graphs in Multiagent Systems

   Akgün, O.E.; Dayı, A.K.; Gil, S.; Nedíc, A. (January 2023, 5th Annual Conference on Learning for Dynamics and Control)

   We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results for the case of directed graphs and where stochastic side information, or observations of trust, is available. We refer to this as “learning trust” since agents must identify which neighbors in the network are reliable, and we derive a protocol to achieve this. We also provide analytical results showing that under this protocol i) agents can learn the legitimacy of all other agents almost surely, and that ii) the opinions of the agents converge in mean to the true legitimacy of all other agents in the network. Lastly, we provide numerical studies showing that our convergence results hold in practice for various network topologies and variations in the number of malicious agents in the network. Keywords: Multiagent systems, adversarial learning, directed graphs, networked systems 

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5. Search on a Line by Byzantine Robots

   https://doi.org/10.1142/S0129054121500209  

   Czyzowicz, Jurek; Georgiou, Konstantinos; Kranakis, Evangelos; Krizanc, Danny; Narayanan, Lata; Opatrny, Jaroslav; Shende, Sunil (January 2021, International Journal of Foundations of Computer Science)

   null (Ed.)

   We consider the problem of fault-tolerant parallel search on an infinite line by n robots. Starting from the origin, the robots are required to find a target at an unknown location. The robots can move with maximum speed 1 and can communicate wirelessly among themselves. However, among the n robots, there are f robots that exhibit byzantine faults. A faulty robot can fail to report the target even after reaching it, or it can make malicious claims about having found the target when in fact it has not. Given the presence of such faulty robots, the search for the target can only be concluded when the non-faulty robots have sufficient evidence that the target has been found. We aim to design algorithms that minimize the value of S_d(n, f), the time to find a target at a (unknown) distance d from the origin by n robots among which f are faulty. We give several different algorithms whose running time depends on the ratio f/n, the density of faulty robots, and also prove lower bounds. Our algorithms are optimal for some densities of faulty robots. 

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