Computer systems are increasingly central to national infrastructure in the financial, medical, manufacturing, defense, and other domains. This infrastructure is at risk from sophisticated cyber-adversaries backed by powerful nation-states, whose capabilities rapidly evolve, demanding equally rapid responses. This calls for advances in artificial intelligence and autonomous reasoning that are tightly integrated with advanced security techniques to identify and correct vulnerabilities, detect threats and attribute them to adversaries, and mitigate and recover from attacks. The ACTION Institute will develop novel approaches that leverage artificial intelligence?informed by and working with experts in security operations?to perform security tasks rapidly and at scale, anticipating the moves of an adversary and taking corrective actions to protect the security of computer networks as well as people?s safety. The Institute will function as a nexus for the AI and cybersecurity communities, and its research efforts will be complemented by innovation in education from K-12 to postdoctoral students, the development of new tools for workforce development, and the creation of new opportunities for collaboration among the Institute?s organizations and with external industry partners.

The AI Institute will initiate a revolutionary approach to cybersecurity, in which AI-enabled intelligent security agents cooperate with humans across the cyber-defense life cycle to jointly improve the security posture of complex computer systems over time. Intelligent security agents will follow a new paradigm of continuous, lifelong learning both autonomously and in collaboration with human experts, supported by a shared knowledge bank and an integrated AI stack that provides novel fundamental primitives for (1) reasoning and learning that incorporates domain knowledge, (2) human-agent interaction, (3) multi-agent collaboration, and (4) strategic gaming and tactical planning. Over time, these intelligent security agents will improve their domain knowledge, becoming increasingly robust and effective in the face of changes in the adversaries? modes of operation, composing defense strategies and tactical plans in the presence of uncertainty, collaborating with each other and with humans for mutually complementary teaming, and adapting to unfamiliar and novel attacks.

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A key research challenge in human-AI systems is the current limitation in data, models and theories that explain their dynamic behavior, coordination, and performance. We do not yet fully understand the dominant socio-cognitive processes that determine the dynamic, adaptive, and learning behavior of human-AI teams.  Of especial interest are decision-making problems in intellective tasks with uncertainty and limited resources: what are the rational, efficient, or irrational strategies and heuristics that humans tend to adopt in such circumstances? Useful socio-cognitive models should inform the design of efficient AI agents that improve the overall human-AI team performance. In other words, empirically-validated models and theories are needed to model and build the human-AI teams of the future and to intervene when their performance deteriorates. The project's broad objective is the development and experimental validation of a theory of coordination of human-AI teams in complex intellective tasks.  We plan to combine fundamental insights and models of team behavior from social sciences with state-of-the-art machine learning and dynamical systems methods.  Specifically, our objective include: 1. modeling socio-cognitive structures in human-AI teams, including transactive memory systems, influence systems, and prospect theories; 2. identifying leading cognitive processes, heuristics and biases that underlie the formation of socio-cognitive structures and affect the accuracy of human-AI team decision making; 3. designing supervisory/coordinating AI agents in human-AI teams, based on concepts from applied psychology and machine learning, and testing/validating them in sequential, risky, uncertain decision making tasks; and 4. modeling how human-AI teams cope with limited training data acquired over short sessions, including how they react to various manipulations and intervention schemes.

Funded by: Army Research Office.

Multidisciplinary University Research Initiative (MURI) on the Network Science of Teams

The recent convergence of research in social and psychological sciences, dynamic and quantitative modeling, and network science has led to a re-examination of collective team behavior from a quantitative and systems-oriented viewpoint. Teams cannot be understood fully by studying their components (members) in isolation: team performance is not simply a sum of individual performances; and a diversity of opinions among members leads to better group outcomes. However, it is not yet understood how patterns of interactions and relationships among team members (i.e. team networks) impact performance. Understanding these patterns is critical, as the resolution of complex issues requires deliberative within-group interaction processes in which alternative courses of action are surfaced, evaluated, and acted upon. This project aims to build quantifiable informative models of teams as dynamical systems interacting over multiple networks, analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance, and break new ground in team design by scaling teams to solve complex tasks (i.e. teams of teams), and advancing social science theories of team performance. Besides UCSB, our team includes leading scientists from MIT, USC, UIUC, and Northwestern.

Funded by: Army Research Office, Award# W911NF-15-1-0577

Predicting network structure and flows on edges is a precondition to effective planning and disaster response in critical infrastructure networks. Accordingly, we propose to develop algorithms inspired by network science, physical modeling, and machine learning to determine this information from incomplete observations. Using both physical domain-specific and data-driven approaches, these algorithms will address diverse problems including: the reconstruction of network topology and parameters, the estimation of network flows, the optimal selection and scheduling of multiple types of sensors, and the modeling of interdependencies in multilayer networks. Together these methods will develop a rigorous theory for solving the inverse problem of edge and flow determination from observations of critical infrastructure networks.

Funded by: Defense Threat Reduction Agency, Award# HDTRA1-19-1-0017

The proposed research will develop novel methods for analyzing and modeling heterogeneous dynamic networked data. Network data arises in a number of application domains ranging from IoT, cloud computing, software analysis, neuroscience, biology, geography, to social sciences. Accordingly, network analysis has emerged as a major paradigm for exploring complex processes behind observed data. Compared to high dimensional data, analysis of network data is more challenging due to interdependencies between entities, the presence of attributes, and the natural evolution of networks over time. The goal of the project will be to understand and model the heterogeneity of behaviors in dynamic networks. The project will have a transformative impact on big data problems that are enabled by a network-centric approach to exploiting dynamic, heterogeneous data, such as brain networks.

The project’s focus is on heterogeneity in dynamic networks: heterogeneity of node behaviors across network structure and time, heterogeneity of the coupling of structure and attributes, and heterogeneity across networks. Against this backdrop, the project will consider the basic problems of clustering (partitioning), classification/regression, decomposition of networks into its basis elements, and the problem of explaining global network behaviors by small network fragments. These problems will be considered for a single network and for multiple networks. Within a network, heterogeneity is observed when nodes or clusters exhibit different behaviors, for instance due to hidden or missing data. Across networks, heterogeneity is observed in the diversity of subject populations or among network instances. The first research thrust will apply spectral theory for partitioning attributed and dynamic networks. The second research thrust will apply convex optimization to find clusters while tolerating heterogeneity across network structure and time. It will also develop methods for estimating graphical models for multiple dynamic networks. The final research thrust will focus on the discovery of succinct sub-networks that are predictive and that evolve concurrently with the underlying networks.

Funded by: National Science Foundation, IIS, Award# 1817046.