Securing multi-robot systems with inter-robot observations and accusations

In various industries, such as manufacturing, logistics, agriculture, defense, search and rescue, and transportation, Multi-robot systems (MRSs) are increasingly gaining popularity. These systems involve multiple robots working together towards a shared objective, either autonomously or under human supervision. However, as MRSs operate in uncertain or even adversarial environments, and the sensors and actuators of each robot may be error-prone, they are susceptible to faults and security threats unique to MRSs. Classical techniques from distributed systems cannot detect or mitigate these threats. In this dissertation, novel techniques are proposed to enhance the security and fault-tolerance of MRSs through inter-robot observations and accusations. A fundamental security property is proposed for MRSs, which ensures that forbidden deviations from a desired multi-robot motion plan by the system supervisor are detected. Relying solely on self-reported motion information from the robots for monitoring deviations can leave the system vulnerable to attacks from a single compromised robot. The concept of co-observations is introduced, which are additional data reported to the supervisor to supplement the self-reported motion information. Co-observation-based detection is formalized as a method of identifying deviations from the expected motion plan based on discrepancies in the sequence of co-observations reported. An optimal deviation-detecting motion planning problem is formulated that achieves all the original application objectives while ensuring that all forbidden plan-deviation attacks trigger co-observation-based detection by the supervisor. A secure motion planner based on constraint solving is proposed as a proof-of-concept to implement the deviation-detecting security property. The security and resilience of MRSs against plan deviation attacks are further improved by limiting the information available to attackers. An efficient algorithm is proposed that verifies the inability of an attacker to stealthily perform forbidden plan deviation attacks with a given motion plan and announcement scheme. Such announcement schemes are referred to as horizon-limiting. An optimal horizon-limiting planning problem is formulated that maximizes planning lookahead while maintaining the announcement scheme as horizon-limiting. Co-observations and horizon-limiting announcements are shown to be efficient and scalable in protecting MRSs, including systems with hundreds of robots, as evidenced by a case study in a warehouse setting. Lastly, the Decentralized Blocklist Protocol (DBP), a method for designing Byzantine-resilient decentralized MRSs, is introduced. DBP is based on inter-robot accusations and allows cooperative robots to identify misbehavior through co-observations and share this information through the network. The method is adaptive to the number of faulty robots and is widely applicable to various decentralized MRS applications. It also permits fast information propagation, requires fewer cooperative observers of application-specific variables, and reduces the worst-case connectivity requirement, making it more scalable than existing methods. Empirical results demonstrate the scalability and effectiveness of DBP in cooperative target tracking, time synchronization, and localization case studies with hundreds of robots. The techniques proposed in this dissertation enhance the security and fault-tolerance of MRSs operating in uncertain and adversarial environments, aiding in the development of secure MRSs for emerging applications

Active Goal Recognition Design

In Goal Recognition Design (GRD), the objective is to modify a domain to facilitate early detection of the goal of a subject agent. Most previous work studies this problem in the offline setting, in which the observing agent performs its interventions before the subject begins acting. In this thesis, we generalize GRD to the online setting in which time passes and the observer\u27s actions are interleaved with those of the subject. We illustrate weaknesses of existing metrics for GRD and propose an alternative better suited to online settings. We provide a formal definition of this Active GRD (AGRD) problem and propose both an optimal algorithm and a suboptimal algorithm for solving it. AGRD occupies an interesting middle ground between passive goal recognition and strategic two-player game settings

In Defense of Breakups: Administering a “Radical” Remedy

Calls for breaking up monopolies—especially Amazon, Facebook, and Google—have largely focused on proving that past acquisitions of companies like Whole Foods, Instagram, and YouTube were anticompetitive. But scholars have paid insufficient attention to another major obstacle that also explains why the government in recent decades has not broken up a single large company. After establishing that an anticompetitive merger or other act has occurred, there is great skepticism of breakups as a remedy. Judges, scholars, and regulators see a breakup as extreme, frequently comparing the remedy to trying to “unscramble eggs.” They doubt the government’s competence in executing such a difficult task, pointing to decision-making flaws dating back to the breakups of Standard Oil in 1911 and AT&T in 1984. Even many scholars calling for more vigorous antitrust enforcement recommend alternative remedies. This Article asserts that the pervasive hesitancy about administering breakups renders antitrust impotent in the face of monopolies—too often a statutory right without a remedy. More importantly, the Article challenges the perception of breakups as unadministrable. The intellectual foundations for the anti-breakup stance are weak, relying on outdated, anecdotal evidence. Moreover, antitrust needs a methodological shift toward paying greater attention to the breakup insights yielded by other disciplines. In particular, business scholars have studied how the world’s leading companies regularly break themselves up voluntarily. Additionally, administrative law scholarship has observed a broader evolution toward collaborative regulation that shows how the much-maligned historical approaches to antitrust remedies could be greatly improved by relying more on the business sector in designing and implementing breakups. In other words, insights from outside of antitrust address many critiques of breakups and show how that remedy is far from radical and messy. Antitrust observers should thus abandon the worldview that compares breaking up prior companies to unscrambling eggs. Or at a minimum they should recognize that scrambled eggs, once cooked, are regularly divided into smaller portions. A greater willingness to do the same to monopolies in the post-merger context and beyond would bring regulators more in line with the business sector, which sees divestitures as a routine part of effective governance

Adversarial Machine Learning For Advanced Medical Imaging Systems

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems. We start by describing our advanced medical imaging systems to tackle the major challenges of on-device deployment: automation, uncertainty, and resource constraint. It is followed by novel unsupervised and semi-supervised robust training schemes to enhance the adversarial robustness of these medical imaging systems. These methods are designed to tackle the unique challenges of defending against adversarial attacks on medical imaging systems and are sufficiently flexible to generalize to various medical imaging modalities and problems. We continue on developing novel training scheme to enhance adversarial robustness of the general DNN based natural image classification models. Based on a unique insight into the predictive behavior of DNNs that they tend to misclassify adversarial samples into the most probable false classes, we propose a new loss function as a drop-in replacement for the cross-entropy loss to improve DNN\u27s adversarial robustness. Specifically, it enlarges the probability gaps between true class and false classes and prevents them from being melted by small perturbations. Finally, we conclude the dissertation by summarizing original contributions and discussing our future work that leverages DNN interpretability constraint on adversarial training to tackle the central machine learning problem of generalization gap

A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles

Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

Tennessee Forest Roundtable : analysis of a multiple stakeholder dialogue on forest resource policy

Like other states, Tennessee has experienced some conflict over forest resource management that has or could result in courtroom litigation, legislative action, and/or protests. Responding to this confrontational atmosphere, a diverse, national coalition of forest stakeholders developed a model for convening local roundtables to provide preliminary input to the Seventh American Forest Congress, planned for February of 1996. This process was designed to bring diverse groups of stakeholders in America\u27s forests together to seek common ground on the future use and management of forests and associated resources. A committee of eleven diverse individuals organized and executed one of these meetings, the Tennessee Forest Roundtable, on November 11, 1995. To their credit, the organizing committee succeeded in planning and executing the roundtable with tight constraints on time, but time pressures prohibited all committee members from actively contributing in all decisions and may have limited the group\u27s cohesiveness. Thirty-nine individuals representing a range of stakeholders in Tennessee\u27s forest resources attended the program, and through the facilitated process developed some common ground. This study examines the Tennessee Forest Roundtable as a process for developing common ground between diverse stakeholders and examines the content of the resulting consensus statements and unresolved issues. The process proved to be one effective way to engage this group of stakeholders in dialogue about the resources that they all value. Though the participants did not represent the \u27ideal\u27 distribution envisioned by the organizing committee, they did represent a broad range of stakeholders. The only group identified in the \u27ideal\u27 that was not represented was that of recreation/tourism interests. Several other groups identified in the \u27ideal\u27 were under represented, and the participants did not reflect the general demographics of Tennessee regarding sex, race, or age. Overall, the process\u27s use of small groups and facilitators worked effectively, but not all participants were equally satisfied with the quality of facilitators. Time constraints limited the program from its conception and continued to be important throughout the program. The day\u27s planned activities were cut short which is thought to have limited the range of common ground which was identified. Facilitators, committee members, and participants all suggested that more time would be beneficial in developing common ground and understanding. Despite these constraints, participants did develop forty-three consensus statements. This common ground clearly demonstrated that these participants believe strongly in both using and caring for forests and their resources, i.e., using the forest to meet human wants and needs but maintaining forest health and sustainability. The remaining comments, on which participants did not deliberate or could not agree, suggest that stakeholders have great interest in forest policy, management, and use. These unresolved issues also suggest that disagreements remain about how society is to balance the needs of human and natural communities. They also display the ambivalence surrounding how society is to balance private and public rights and responsibilities. Both the process and content analysis suggest that though these forest stakeholders have taken some crucial first steps toward developing a collaborative community, more time and energy must be invested if the stakeholders in Tennessee\u27s forest resources are to truly collaborate on the use and management of these resources which so many value