5,025 research outputs found
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Decision analysis deals with modeling and enhancing decision processes. A
principal challenge in improving behavior is in obtaining a transparent
description of existing behavior in the first place. In this paper, we develop
an expressive, unifying perspective on inverse decision modeling: a framework
for learning parameterized representations of sequential decision behavior.
First, we formalize the forward problem (as a normative standard), subsuming
common classes of control behavior. Second, we use this to formalize the
inverse problem (as a descriptive model), generalizing existing work on
imitation/reward learning -- while opening up a much broader class of research
problems in behavior representation. Finally, we instantiate this approach with
an example (inverse bounded rational control), illustrating how this structure
enables learning (interpretable) representations of (bounded) rationality --
while naturally capturing intuitive notions of suboptimal actions, biased
beliefs, and imperfect knowledge of environments
World Bank Borrower Relations and Project Supervision
This paper explores the relevance of the principal-agent model for analyzing development projects using data from World Bank-funded projects. After demonstrating that World Bank loan agreements can be viewed as principal-agent contracts, the paper explores the importance of the agency problem in determining project performance. Predictions from an adversarial model contrast with those of a cooperative model. The importance of information in the adversarial model links World Bank supervision to project performance. Data support the relevance of the agency problem and the role of supervision as monitoring. The paper concludes with suggestions for modifying project selection and implementation to reduce agency problems.
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