Supporting tactical intelligence using collaborative environments and social networking

Wollocko, Arthur; Farry, Michael; Stärk, Robert F.

doi:10.1117/12.2015997

Cited by 6 publications

(5 citation statements)

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“…Analysts are increasingly well versed in modern collaboration environments and social media, and systems are emerging that seek to combine the benefits of these approaches with existing software tools and processes for structuring and supporting tactical intelligence analysis. A recent example of this in [19] seeks to enable analysts to identify the decision-relevant data scattered among databases and the mental models of other personnel by employing familiar social media-style collaboration techniques. There is some evidence to indicate that not only is it useful to collaborate within the same analyst team/group, but, when collaboration is extended to the crowd and mediated by an intelligent software agent, the outcome of the intelligence analysis can be greatly improved [20].…”

Section: Related Workmentioning

confidence: 99%

Sherlock: Experimental Evaluation of a Conversational Agent for Mobile Information Tasks

Preece

Webberley

Braines

et al. 2017

IEEE Trans. Human-Mach. Syst.

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Controlled natural language (CNL) has great potential to support human-machine interaction (HMI) because it provides an information representation that is both human readable and machine processable. We investigated the effectiveness of a CNL-based conversational interface for HMI in a behavioral experiment called simple human experiment regarding locally observed collective knowledge (SHERLOCK). In SHERLOCK, individuals acted in groups to discover and report information to the machine using natural language (NL), which the machine then processed into CNL. The machine fused responses from different users to form a common operating picture, a dashboard showing the level of agreement for distinct information. To obtain information to add to this dashboard, users explored the real world in a simulated crowdsourced sensing scenario. This scenario represented a simplified controlled analog for tactical intelligence (i.e., direct intelligence of the environment), which is key for rapidly planning military, law enforcement, and emergency operations. Overall, despite close to zero training, 74% of the users inputted NL that was machine interpretable and addressed the assigned tasks. An experimental manipulation aimed to increase user-machine interaction, however, did not improve performance as hypothesized. Nevertheless, results indicate that the conversational interface may be effective in assisting humans with collection and fusion of information in a crowdsourcing context. Index Terms-Controlled natural language (CNL), conversational interface, human-computer collaboration (HCC), humanmachine interaction (HMI), tactical intelligence.

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Section: Related Workmentioning

confidence: 99%

Sherlock: Experimental Evaluation of a Conversational Agent for Mobile Information Tasks

Preece

Webberley

Braines

et al. 2017

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

show abstract

“…A recent example of this seeks to enable analysts to identify the decision-relevant data scattered among databases and the mental models of other personnel by employing familiar social media-style collaboration techniques. 16 There is some evidence to indicate that, not only is it useful to collaborate within the same analyst team, but when collaboration is extended to the crowd and mediated by an intelligent software agent, the outcome of the intelligence analysis can be greatly improved. 17 In the context of current concerns regarding transparency in big data approaches, 7 clearly a significant issue in the use of all open source intelligence concerns the potential for misinformation.…”

Section: Background and Related Workmentioning

confidence: 99%

From open source communications to knowledge

Preece

Roberts

Rogers

et al. 2016

Next-Generation Analyst IV

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Rapid processing and exploitation of open source information-including social media sources-in order to shorten decision-making cycles, has emerged as an important issue in intelligence analysis in recent years. Through a series of case studies and natural experiments, focussed primarily upon policing and counter-terrorism scenarios, we have developed an approach to information foraging and framing to inform decision making, drawing upon open source intelligence-in particular Twitter, due to its real-time focus and frequent use as a carrier for links to other media. Our work uses a combination of natural language (NL) and controlled natural language (CNL) processing to support information collection from human sensors, linking and schematising of collected information, and the framing of situational pictures. We illustrate the approach through a series of vignettes, highlighting (1) how relatively lightweight and reusable knowledge models (schemas) can rapidly be developed to add context to collected social media data, (2) how information from open sources can be combined with reports from trusted observers, for corroboration or to identify conflicting information; and (3) how the approach supports users operating at or near the tactical edge, to rapidly task information collection and inform decision-making. The approach is supported by bespoke software tools for social media analytics and knowledge management.

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“…Previously researched difficulties that exist in PED relate to challenges encountered when operating through multiple communication channels, maintaining a heightened sense of situation awareness in a dynamic, fast-paced, high stress environment, and the overload that can occur having to quickly annotate and analyze constant streams of collected data [6] . With these organizational complexities, difficulties are also present in the increased amount of human-automation teaming within the military domain that will only continue to increase in future years [3] .…”

Section: Ped Analysismentioning

confidence: 99%

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

et al. 2016

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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&D priorities to increase the throughput of the intelligence enterprise are discussed.

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Supporting tactical intelligence using collaborative environments and social networking

Cited by 6 publications

References 0 publications

Sherlock: Experimental Evaluation of a Conversational Agent for Mobile Information Tasks

Sherlock: Experimental Evaluation of a Conversational Agent for Mobile Information Tasks

From open source communications to knowledge

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

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