Ranjeev Mittu

The Challenge of Scalable and Distributed Fusion of Disparate Sources of Information

A key enabler for Network Centric Warfare (NCW) is a sensor network that can collect and fuse vast amounts of disparate and complementary information from sensors that are geographically dispersed throughout the battlespace. This information will lead to better situation awareness so that commanders will be able to act faster and more effectively. However, these benefits are possible only if the sensor data can be fused and synthesized for distribution to the right user in the right form at the right time within the constraints of available bandwidth. In this paper we consider the problem of developing Level 1 data fusion algorithms for disparate fusion in NCW. These algorithms must be capable of operating in a fully distributed (or decentralized) manner; must be able to scale to extremely large numbers of entities; and must be able to combine many disparate types of data. To meet these needs we propose a framework that consists of three main components: an attribute-based state representation that treats an entity state as a collection of attributes, new methods or interpretations of uncertainty, and robust algorithms for distributed data fusion. We illustrate the discussion in the context of maritime domain awareness, mobile adhoc networks, and multispectral image fusion.

Multi-agent systems in mobile ad hoc networks

A number of technologies are evolving that will help formulate more adaptive and robust network architectures intended to operate in dynamic, mobile environments. One technology area, mobile ad hoc networking (MANET) enables self-organizing, multi-hop heterogeneous network routing services and organization. Such technology is important in future DoD networking, especially in the forward edge of the battlespace where self-organizing, robust networking is needed. A second technology area, multi-agent systems (MAS) can enable autonomous, team-based problem solving under varying environmental conditions. Previous work done in MAS has assumed relatively benign wired network behavior and inter-agent communications characteristics that may not be well supported in MANET environments. In addition, the resource costs associated with performing inter-agent communications have a more profound impact in a mobile wireless environment. The combined operation of these technology areas, including cross-layer design considerations, has largely been unexplored to date. This paper describes ongoing research to improve the ability of these technologies to work in concert. An outline of various design and system architecture issues is first presented. We then describe models, agent systems, MANET protocols, and additional components that are being applied in our research. We present an analysis method to measure agent effectiveness and early evaluations of working prototypes within MANET environments. We conclude by outlining some open issues and areas of further work

The Human Factor in Cybersecurity: Robust & Intelligent Defense

In this chapter, we review the pervasiveness of cyber threats and the roles of both attackers and cyber users (i.e. the targets of the attackers); the lack of awareness of cyber-threats by users; the complexity of the new cyber environment, including cyber risks; engineering approaches and tools to mitigate cyber threats; and current research to identify proactive steps that users and groups can take to reduce cyber-threats. In addition, we review the research needed on the psychology of users that poses risks to users from cyber-attacks. For the latter, we review the available theory at the individual and group levels that may help individual users, groups and organizations take actions against cyber threats. We end with future research needs and conclusions. In our discussion, we first agreed that cyber threats are making cyber environments more complex and uncomfortable for average users; second, we concluded that various factors are important (e.g., timely actions are often necessary in cyber space to counter the threats of the attacks that commonly occur at internet speeds, but also the ‘slow and low’ attacks that are difficult to detect, threats that occur only after pre-specified conditions have been satisfied that trigger an unsuspecting attack). Third, we concluded that advanced persistent threats (APTs) pose a risk to users but also to national security (viz., the persistent threats posed by other Nations). Fourth, we contend that using “red” teams to search cyber defenses for vulnerabilities encourages users and organizations to better defend themselves. Fifth, the current state of theory leaves many questions unanswered that researchers must pursue to mitigate or neutralize present and future threats. Lastly, we agree with the literature that cyber space has had a dramatic impact on American life and that the cyber domain is a breeding ground for disorder. However, we also believe that actions by users and researchers can be taken to stay safe and ahead of existing and future threats.

Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations

Greater unmanned system autonomy will lead to improvements in mission outcomes, survivability and safety. However, an increase in platform autonomy increases system complexity. For example, flexible autonomous platforms deployed in a range of environments place a burden on humans to understand evolving behaviors. More importantly, when problems arise within complex systems, they need to be managed without increasing operator workload. A supervisory control paradigm can reduce workload and allow a single human to manage multiple autonomous platforms. However, this requires consideration of the human as an integrated part of the overall system, not just as a central controller. This paradigm can benefit from novel and intuitive techniques that isolate and predict anomalous situations or state trajectories within complex autonomous systems in terms of mission context to allow efficient management of aberrant behavior. This information will provide the user with improved feedback about system behavior, which will in turn lead to more relevant and effective prescriptions for interaction, particularly during emergency procedures. This, in turn, will enable proper trust calibration. We also argue that by understanding the context of the user’s decisions or system’s actions (seamless integration of the human), the autonomous platform can provide more appropriate information to the user.

Learning and coordination: An overview

Adaptive learning techniques can automate the large-scale coordination of multi-agent systems and enhance their robustness in dynamic environments. This paper surveys several learning approaches that have been developed to address three different aspects of coordination, namely, learning coordination behavior, team learning, and the integrated learning of trust and reputation in order to facilitate coordination in open systems including collaborative systems where artificial agents and humans interact. Although convergence in multi-agent learning is still an open research question, several applications have emerged using some of the learning techniques presented.

Robust Intelligence and Trust in Autonomous Systems

This volume explores the intersection of robust intelligence (RI) and trust in autonomous systems across multiple contexts among autonomous hybrid systems, where hybrids are arbitrary combinations of humans, machines and robots. To better understand the relationships between artificial intelligence (AI) and RI in a way that promotes trust between autonomous systems and human users, this book explores the underlying theory, mathematics, computational models, and field applications. It uniquely unifies the fields of RI and trust and frames it in a broader context, namely the effective integration of human-autonomous systems. A description of the current state of the art in RI and trust introduces the research work in this area. With this foundation, the chapters further elaborate on key research areas and gaps that are at the heart of effective human-systems integration, including workload management, human computer interfaces, team integration and performance, advanced analytics, behavior modeling, training, and, lastly, test and evaluation. Written by international leading researchers from across the field of autonomous systems research, Robust Intelligence and Trust in Autonomous Systems dedicates itself to thoroughly examining the challenges and trends of systems that exhibit RI, the fundamental implications of RI in developing trusted relationships with present and future autonomous systems, and the effective human systems integration that must result for trust to be sustained. Contributing authors: David W. Aha, Jenny Burke, Joseph Coyne, M.L. Cummings, Munjal Desai, Michael Drinkwater, Jill L. Drury, Michael W. Floyd, Fei Gao, Vladimir Gontar, Ayanna M. Howard, Mo Jamshidi, W.F. Lawless, Kapil Madathil, Ranjeev Mittu, Arezou Moussavi, Gari Palmer, Paul Robinette, Behzad Sadrfaridpour, Hamed Saeidi, Kristin E. Schaefer, Anne Selwyn, Ciara Sibley, Donald A. Sofge, Erin Solovey, Aaron Steinfeld, Barney Tannahill, Gavin Taylor, Alan R. Wagner, Yue Wang, Holly A. Yanco, Dan Zwillinger.

Unclassified Information Sharing and Coordination in Security, Stabilization, Transition and Reconstruction Efforts

The emergence of new doctrine is enabling security, stabilization, transition and reconstruction (SSTR) operations to become a core U.S. military mission. These operations are now given equal priority to combat operations. The immediate goal in SSTR is to provide the local populace with security, restore essential services, and meet humanitarian needs. The long-term goal is to help develop indigenous capacity for securing and providing essential services, therefore, many SSTR operations are best performed by indigenous groups with support from foreign agencies and professionals. Large scale disasters, however, are an example where military support can enhance the value of SSTR operations. Without the means to effectively coordinate groups across the civil-military boundary, basic assistance and relief operations may be severely impeded. This paper will describe a conceptual portal, ShareInfoForPeople, which incorporates advanced Information and Communication Technology to enable collaboration, coordination and information sharing across the civil-military boundary in support of SSTR.

Coordination in Disaster Management and Response: A Unified Approach

Natural, technological and man-made disasters are typically followed by chaos that results from an inadequate overall response. Three separate levels of coordination are addressed in the mitigation and preparedness phase of disaster management where environmental conditions are slowly changing: (1) communication and transportation infrastructure, (2) monitoring and assessment tools, (3) collaborative tools and services for information sharing. However, the nature of emergencies is to be unpredictable. Toward that end, a fourth level of coordination --- distributed resource/role allocation algorithms of first responders, mobile workers, aid supplies and victims --- addresses the dynamic environmental conditions of the response phase during an emergency. A tiered peer-to-peer system architecture could combine those different levels of coordination to address the changing needs of disaster management. We describe in this paper the architecture of such a tiered peer-to-peer agent-based coordination decision support system for disaster management and response and the applicable coordination algorithms including ATF, a novel, self-organized algorithm for adaptive team formation.

Coordination Challenges and Issues in Stability, Security, Transition and Reconstruction (SSTR) and Cooperative Unmanned Aerial Vehicles

SSTR operations are becoming a priority for the Department of Defense (DoD) and are being given the same stature as the more traditional combat operations. The near-term goal of SSTR is to provide the local populace with security, restore essential services, and meet humanitarian needs, while in the longer term ensure a stable infrastructure. Large scale disasters are an example where SSTR operations can provide value. In such scenarios, local governments and non-governmental institutions are under great stress in order to respond in a timely manner to provide basic relief to the affected communities, and there may be outburst of aid from the local groups. All of this can lead to a less than desirable outcome if poorly coordinated! The use of unmanned aerial vehicles (UAVs) to support intelligence, surveillance and reconnaissance (ISR) is becoming increasingly important. These assets can enable the collection of needed information for the execution of a given set of tasks. In large scale operations, however, the ability for the UAVs to self-coordinate may be needed as it will be difficult for human operators to effectively control large teams of UAVs. This paper will begin by introducing some of the key aspects of multiagent coordination, with a focus on the operational challenges with regard to SSTR such as disaster management response as well as UAV coordination. We will then discuss the coordination challenges and gaps in order to motivate an adaptive, multiagent based approach to coordination as well as additional opportunities for research. We will conclude with a brief summary

Defeating information overload in health surveillance using a metacognitive aid innovation from military combat systems

Modern sensors for health surveillance generate high volumes and rates of data that currently overwhelm operational decision-makers. These data are collected with the intention of enabling front-line clinicians to make effective clinical judgments. Ironically, prior human–systems integration (HSI) studies show that the flood of data degrades rather than aids decision-making performance. Health surveillance operations can focus on aggregate changes to population health or on the status of individual people. In the case of clinical monitoring, medical device alarms currently create an information overload situation for front-line clinical workers, such as hospital nurses. Consequently, alarms are often missed or ignored, and an impending patient adverse event may not be recognized in time to prevent crisis. One innovation used to improve decision making in areas of data-rich environments is the Human Alerting and Interruption Logistics (HAIL) technology, which was originally sponsored by the US Office of Naval Research. HAIL delivers metacognitive HSI services that empower end-users to quickly triage interruptions and dynamically manage their multitasking. HAIL informed our development of an experimental prototype that provides a set of context-enabled alarm notification services (without automated alarm filtering) to support users’ metacognition for information triage. This application is called HAIL Clinical Alarm Triage (HAIL-CAT) and was designed and implemented on a smartwatch to support the mobile multitasking of hospital nurses. An empirical study was conducted in a 20-bed virtual hospital with high-fidelity patient simulators. Four teams of four registered nurses (16 in total) participated in a 180-minute simulated patient care scenario. Each nurse was assigned responsibility to care for five simulated patients and high rates of simulated health surveillance data were available from patient monitors, infusion pumps, and a call light system. Thirty alarms per nurse were generated in each 90-minute segment of the data collection sessions, only three of which were clinically important alarms. The within-subjects experimental design included a treatment condition where the nurses used HAIL-CAT on a smartwatch to triage and manage alarms and a control condition without the smartwatch. The results show that, when using the smartwatch, nurses responded three times faster to clinically important and actionable alarms. An analysis of nurse performance also shows no negative effects on their other duties. Subjective results show favorable opinions about utility, usability, training requirement, and adoptability. These positive findings suggest the potential for the HAIL HSI system to be transferrable to the domain of health surveillance to achieve the currently unrealized potential utility of high-volume data.