Stephen Russell’s research while affiliated with Jackson health system and other places

We consider the use of Shannon information theory, and its various entropic terms to aid in reaching optimal decisions that should be made in a multi-agent/Team scenario. The methods that we use are to model how various agents interact, including power allocation. Our metric for agents passing information are classical Shannon channel capacity. Our results are the mathematical theorems showing how combining agents influences the channel capacity.

The internet of battlefield things (IoBT) is expected to be a major feature of future tactical wireless networks. Multiple challenges arise from the expected scale, heterogeneity, information sharing (in a joint or coalition environment), dynamics, and actions of sophisticated adversaries. The chapter will discuss these challenges in detail, and explore emerging directions which provide opportunities to enable a scalable, secure, and performant IoBT.

We study the problem of channel uncertainty on wireless transmissions from different users with mutual interference. Specifically, the channel gains from the transmitters to the receivers are available only through their mean and covariance rather than complete distributions. Our goal is to maximize the energy efficiency among all transmitter-receiver pairs while guaranteeing their capacity requirements. For this problem, we employ chance-constrained programming (CCP), which allows occasional violation of target capacity threshold as long as the probability of such violation is below a small tolerable constant (risk level). We propose a solution based on a novel reformulation technique that converts the original CCP into a deterministic optimization problem without relaxation errors. Then the deterministic optimization problem is approximated into a Geometric Program (GP) based on tight polynomial approximations, which can be solved optimally. We prove that our proposed solution achieves near-optimal performance with polynomial time complexity.

The automation of safety-critical systems is becoming increasingly prevalent as machine learning approaches become more sophisticated and capable. However, approaches that are safe to use in critical systems must account for uncertainty. Most real-world applications currently use deterministic machine learning techniques that cannot incorporate uncertainty. In order to place systems in critical infrastructure, we must be able to understand and interpret how machines make decisions. This need is so that they can provide support for human decision-making, as well as the potential to operate autonomously. As such, we highlight the importance of incorporating uncertainty into the decision-making process and present the advantages of Bayesian decision theory. We showcase an example of classifying vehicles from their acoustic recordings, where certain classes have significantly higher threat levels. We show how carefully adopting the Bayesian paradigm not only leads to safer decisions, but also provides a clear distinction between the roles of the machine learning expert and the domain expert.

AI-enabled systems are becoming more pervasive, yet system engineering techniques still face limitations in how AI systems are being deployed. This chapter provides a discussion of the implications of hierarchical component composition and the importance of data in bounding AI system performance and stability. Issues of interoperability and uncertainty are introduced and how they can impact emergent behaviors of AI systems are illustrated through the presentation of a natural language processing (NLP) system used to provide similarity comparisons of organizational corpora. Within the bounds of this discussion, we examine how the concepts from Design science can introduce additional rigor to AI complex system engineering.

The risks from cyber threats are increasing. Cyber threats propagate primarily with deception. Threats from deception derive from insider and outsider attacks. These threats are countered by reactive and proactive cyber-defenses. Alarmingly, increasing proactive cyber defenses simulate conflicts from the Wild West of yesteryear. However, deception is inadequately addressed with traditional theories based on methodological individualism (MI). Worse, MI’s rational choice theory breaks down in the presence of social conflict. In contrast, interdependence theory addresses barriers, deception to penetrate the vulnerabilities of barriers and the conflict which ensues, topics where interdependence theory thrives. Interdependence includes the effects of the constructive or destructive interference that constitute every social interaction. Our research primarily addresses the application of interdependence theory to autonomous human-machine teams (A-HMTs), which entails artificial intelligence (AI) and AI’s sub-field of machine learning (ML). A-HMTs require defenses that protect a team from cyberthreats, adverse interference and other vulnerabilities while affording the opportunity for a team to become autonomous. In this chapter, we focus on an introduction that includes a review of traditional methodological individualism and rational choice theory. Our introduction is followed by a discussion of deception and its implications for reactive cyber defenses. We next cover proactive cyber defenses in a separate section. Then we review our research on interdependence theory and its application to A-HMTs. We conclude that while cyber-risks are increasing, so too is the teamwork that strengthens cyber-defenses. The future belongs to a theory of interdependence that improves cyber-defenses, teams and the science that generalizes to A-HMTs.

We consider an underlay coexistence scenario where secondary users (SUs) must keep their interference to the primary users (PUs) under control. However, the channel gains from the PUs to the SUs are uncertain due to a lack of cooperation between the PUs and the SUs. Under this circumstance, it is preferable to allow the interference threshold of each PU to be violated occasionally as long as such violation stays below a probability. In this article, we employ Chance-Constrained Programming (CCP) to exploit this idea of occasional interference threshold violation. We assume the uncertain channel gains are only known by their mean and covariance. These quantities are slow-changing and easy to estimate. Our main contribution is to introduce a novel and powerful mathematical tool called Exact Conic Reformulation (ECR), which reformulates the intractable chance constraints into tractable convex constraints. Further, ECR guarantees an equivalent reformulation from linear chance constraints into deterministic conic constraints without the limitations associated with Bernstein Approximation, on which our research community has been fixated on for years. Through extensive simulations, we show that our proposed solution offers a significant improvement over existing approaches in terms of performance and ability to handle channel correlations (where Bernstein Approximation is no longer applicable).

Underlay coexistence is a spectrum efficient mechanism to roll out 5G picocells within a macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocells, it is impossible to have complete knowledge of channel conditions between them. Under such a circumstance, chance-constrained programming (CCP) has been shown to be an ideal optimization tool to address such a channel uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G's timing requirement (125 s). To address this problem, we propose a novel scheduler called GPU-based Underlay Coexistence (GUC) with the goal of finding an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small subproblems that are suitable for parallel computation on GPU platforms. By selecting a subset of promising subproblems and solving them in parallel with fast algorithms, we are able to leverage GPU parallel computing and develop a real-time solution. Through extensive experiments, we show that GUC meets the 125 s requirement while achieving 90% optimality on average.