Martin Voshell

Increasing the Transparency of Unmanned Systems: Applications of Ecological Interface Design

This paper describes ongoing efforts to address the challenges of supervising teams of heterogeneous unmanned vehicles through the use of demonstrated Ecological Interface Design EID principles. We first review the EID framework and discuss how we have applied it to the unmanned systems domain. Then, drawing from specific interface examples, we present several generalizable design strategies for improved supervisory control displays. We discuss how ecological display techniques can be used to increase the transparency and observability of highly automated unmanned systems by enabling operators to efficiently perceive and reason about automated support outcomes and purposefully direct system behavior.

Towards Card-based User Interfaces Workspaces for Group Mission Planning

Current mission planning interfaces are difficult to understand, cumbersome to use, and do not support the collaborative aspect of group mission planning. To address this critical shortfall, this paper describes the designed and demonstrated set of card-based user interfaces (card UIs) to increase the effectiveness of group mission planning workflows. These interfaces provide consistent visual structures for a diverse set of tasks across team members, enable team members to understand progress across distributed tasks and facilitate situational awareness of the overall evolving mission plan. This paper describes key considerations for group mission planning activities and present examples of our card UI interface supporting group mission planning tasks.

A Multi-modal Interface for Natural Operator Teaming with Autonomous Robots (MINOTAUR)

Dismounted squads face logistical problems, including the management of physical burdens in complex operating environments. Autonomous unmanned ground vehicles (UGVs) can help transport equipment and supplies, but require active remote control or teleoperation, even for mundane tasks such as long-distance travel. This requires heads down attention, causing fatigue and reducing situational awareness. To address these needs, we designed and prototyped a Multi-modal Interface for Natural Operator Teaming with Autonomous Robots (MINOTAUR). The MINOTAUR human-robot interface (HRI) provides observability and directability of UGV behavior through a multi-modal interface that leverages gesture input, touch/physical input through a watch-based operator control unit (OCU), and voice input. MINOTAUR’s multi-modal approach enables operators to leverage the strengths of each modality, while the OCU enables quick control inputs through lightweight interactions and at-a-glance information status summaries. This paper describes the requirements and use case analysis that informed MINOTAUR designs and provides detailed descriptions of design concepts.

Challenges in Making Policy Decision-Support Systems Operational

Given the increasing complexity of the international policy environment, there are strong incentives to employ modern computation in the form of models and simulation to help analysts address and account for this complexity. While academic examples of such complex system policy models have been available for some time, they are not yet able to be used effectively and consistently in operational policy environments due to a range of technical gaps and challenges including: (1) simulation; (2) Human Machine Interface (HMI); and (3) data. Specific examples for each type of policy model challenge will be developed in this paper.

Combining cognitive engineering and information fusion architectures to build effective joint systems

The Dual Node Decision Wheels (DNDW) architecture concept was previously described as a novel approach toward integrating analytic and decision-making processes in joint human/automation systems in highly complex sociotechnical settings. In this paper, we extend the DNDW construct with a description of components in this framework, combining structures of the Dual Node Network (DNN) for Information Fusion and Resource Management with extensions on Rasmussen’s Decision Ladder (DL) to provide guidance on constructing information systems that better serve decision-making support requirements. The DNN takes a component-centered approach to system design, decomposing each asset in terms of data inputs and outputs according to their roles and interactions in a fusion network. However, to ensure relevancy to and organizational fitment within command and control (C2) processes, principles from cognitive systems engineering emphasize that system design must take a human-centered systems view, integrating information needs and decision making requirements to drive the architecture design and capabilities of network assets. In the current work, we present an approach for structuring and assessing DNDW systems that uses a unique hybrid DNN top-down system design with a human-centered process design, combining DNN node decomposition with artifacts from cognitive analysis (i.e., system abstraction decomposition models, decision ladders) to provide work domain and task-level insights at different levels in an example intelligence, surveillance, and reconnaissance (ISR) system setting. This DNDW structure will ensure not only that the information fusion technologies and processes are structured effectively, but that the resulting information products will align with the requirements of human decision makers and be adaptable to different work settings .

Investigating performance variability of processing, exploitation, and dissemination using a socio-technical systems analysis approach

Data collection processes supporting Intelligence, Surveillance, and Reconnaissance (ISR) missions have recently undergone a technological transition accomplished by investment in sensor platforms. Various agencies have made these investments to increase the resolution, duration, and quality of data collection, to provide more relevant and recent data to warfighters. However, while sensor improvements have increased the volume of high-resolution data, they often fail to improve situational awareness and actionable intelligence for the warfighter because it lacks efficient Processing, Exploitation, and Dissemination and filtering methods for mission-relevant information needs. The volume of collected ISR data often overwhelms manual and automated processes in modern analysis enterprises, resulting in underexploited data, insufficient, or lack of answers to information requests. The outcome is a significant breakdown in the analytical workflow. To cope with this data overload, many intelligence organizations have sought to re-organize their general staffing requirements and workflows to enhance team communication and coordination, with hopes of exploiting as much high-value data as possible and understanding the value of actionable intelligence well before its relevance has passed. Through this effort we have taken a scholarly approach to this problem by studying the evolution of Processing, Exploitation, and Dissemination, with a specific focus on the Army’s most recent evolutions using the Functional Resonance Analysis Method. This method investigates socio-technical processes by analyzing their intended functions and aspects to determine performance variabilities. Gaps are identified and recommendations about force structure and future R and D priorities to increase the throughput of the intelligence enterprise are discussed.

Using Temporal Representations for Understanding Complex Interrelationships for Mission Planning

The ability to analyze, recognize, and plan for operational events—i.e., patterns of change over time—is a critical component of effective situation assessment within the military. Military planners use time when faced with creating, comparing, and deciding on complex planning and re-planning decisions. One way to intuitively convey changes in time based information to planners is with the use of temporal representations, particularly timeline visualizations. Timeline visualizations have been a very popular method in the past because of their ability to provide a clear representation of the causes and effects that occur throughout mission increments which supports planners with re-planning for new events. In this paper, we discuss our unique design concept with using multiple timeline visualizations as a way to support military planners with understanding the complex interrelationships that occur when predicting the timing and availability of mission resources as well as analyzing the effects to unforeseen events. We also discuss future design directions that will incorporate user feedback to improve the system’s usability and better visualize these interrelationships between planned actions.

Employing socially driven techniques for framing, contextualization, and collaboration in complex analytical threads

The proliferation of sensor technologies continues to impact Intelligence Analysis (IA) work domains. Historical procurement focus on sensor platform development and acquisition has resulted in increasingly advanced collection systems; however, such systems often demonstrate classic data overload conditions by placing increased burdens on already overtaxed human operators and analysts. Support technologies and improved interfaces have begun to emerge to ease that burden, but these often focus on single modalities or sensor platforms rather than underlying operator and analyst support needs, resulting in systems that do not adequately leverage their natural human attentional competencies, unique skills, and training. One particular reason why emerging support tools often fail is due to the gap between military applications and their functions, and the functions and capabilities afforded by cutting edge technology employed daily by modern knowledge workers who are increasingly “digitally native.” With the entry of Generation Y into these workplaces, “net generation” analysts, who are familiar with socially driven platforms that excel at giving users insight into large data sets while keeping cognitive burdens at a minimum, are creating opportunities for enhanced workflows. By using these ubiquitous platforms, net generation analysts have trained skills in discovering new information socially, tracking trends among affinity groups, and disseminating information. However, these functions are currently under-supported by existing tools. In this paper, we describe how socially driven techniques can be contextualized to frame complex analytical threads throughout the IA process. This paper focuses specifically on collaborative support technology development efforts for a team of operators and analysts. Our work focuses on under-supported functions in current working environments, and identifies opportunities to improve a team’s ability to discover new information and disseminate insightful analytic findings. We describe our Cognitive Systems Engineering approach to developing a novel collaborative enterprise IA system that combines modern collaboration tools with familiar contemporary social technologies. Our current findings detail specific cognitive and collaborative work support functions that defined the design requirements for a prototype analyst collaborative support environment.

Dual node decision wheels: an architecture for interconnected information fusion and decision making

As the modern information environment continues to expand with new technologies, military Command and Control (C2) has increasing access to unprecedented amounts of data and analytic resources to support military decision making. However, with the increasing quantity and heterogeneity of multi-INT data—from new collection platforms, new sensors, and new analytic tools—comes a growing information fusion challenge. For example, increasingly distributed processing, exploitation, and dissemination (PED) capabilities and analyst intelligence resources must identify and integrate the most relevant data sources to support and improve operational command and control and situation awareness without becoming overwhelmed by data and potentially missing critical information. We present an innovative new information fusion and organizational decision-making architecture—Dual Node Decision Wheels (DNDW)—that integrates multi-INT PED, information analysis, and C2 processes through a novel combination of goal-directed information fusion and data-driven decision making, helping alleviate “big data” challenges through more fluid coordination of organizations and technologies. DNDW applies the dual node network for fusion and resource management with semantic links between organizational processes and decision aides, ensuring that each organizational role has access to the right information. DNDW can map fusion onto any organizational structure and provide a cost-effective solution methodology for integrating new technologies.

Maps, Space-time Cubes, and Meta-Information for Understanding Path Information A Comparative Analysis

Two experiments were conducted to investigate the usefulness of different geospatial representations in interpreting paths and activities of individuals moving through a complex spatial environment. Three-dimensional “space-time cubes” were compared to more traditional two-dimensional map displays both with and without animation/playback of the movements. Additionally, we investigated whether or not providing overlaid meta-information about individuals’ possible locations in between known (“sensed”) locations would improve performance. Results indicated that there were no advantages to, and some indications of increased workload, due to the 3D representation, perhaps because of challenges in interpreting the third (time) dimension. However, providing meta-information about possible locations supported performance when it was necessary for participants to recognize potential meetings or other events that did not occur at sensed locations. Geospatial displays used to support interpretation of movement tracking (e.g., for use in intelligence, surveillance, and reconnaissance applications) should compute and provide meta-information about possible paths between sensed locations. Additionally, if a third (time) dimension is included, additional training and support may be required.