A multi-agent infrastructure for hard and soft information fusion

Rimland, Jeffrey C.; Hall, David L.

doi:10.1117/12.883870

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…Our sampling of AI papers is shown in Table 3: under Army Research Office funding to study and develop prototype capabilities to rigorously deal with the challenges of the hard + soft fusion problem (see [42,43,44] for some overview papers). Robust architectures that provide meaningful automated support for ABI analyses and the evolving ABI tradecraft will need to include not only the tools described herein as derived from directly and indirectly-related applications, but also integrate the formalisms of methods to address the hard + soft multi-INT fusion problem.…”

Section: Figure 6 Conceptual Model Of An Activity (From [39])mentioning

confidence: 99%

Foundational Technologies for Activity-Based Intelligence - A Review of the Literature

Llinas¹,

Scrofani²

2014

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Section: Figure 6 Conceptual Model Of An Activity (From [39])mentioning

confidence: 99%

Foundational Technologies for Activity-Based Intelligence - A Review of the Literature

Llinas¹,

Scrofani²

2014

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“…Agents have a long history, countless publications (including [15], [16], [17], [18], [19], [20], [21], [22], [14], et al), and numerous dedicated conferences (including the ACM International Conference on Autonomous Agents and Multi agent Systems) and multiple related IEEE and ACM journals.…”

Section: Multi Agent Systems (Mas)mentioning

confidence: 99%

Conserving analyst attention units: use of multi-agent software and CEP methods to assist information analysis

2013

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Although the capability of computer-based artificial intelligence techniques for decision-making and situational awareness has seen notable improvement over the last several decades, the current state-of-the-art still falls short of creating computer systems capable of autonomously making complex decisions and judgments in many domains where data is nuanced and accountability is high. However, there is a great deal of potential for hybrid systems in which software applications augment human capabilities by focusing the analyst's attention to relevant information elements based on both a priori knowledge of the analyst's goals and the processing/correlation of a series of data streams too numerous and heterogeneous for the analyst to digest without assistance.Researchers at Penn State University are exploring ways in which an information framework influenced by Klein's (Recognition Primed Decision) RPD model, Endsley's model of situational awareness, and the Joint Directors of Laboratories (JDL) data fusion process model can be implemented through a novel combination of Complex Event Processing (CEP) and Multi-Agent Software (MAS). Though originally designed for stock market and financial applications, the high performance data-driven nature of CEP techniques provide a natural compliment to the proven capabilities of MAS systems for modeling naturalistic decision-making, performing process adjudication, and optimizing networked processing and cognition via the use of "mobile agents." This paper addresses the challenges and opportunities of such a framework for augmenting human observational capability as well as enabling the ability to perform collaborative context-aware reasoning in both human teams and hybrid human / software agent teams.

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“…Although the technology to accomplish this fusion of distributed and human-centric sensing is still largely under development (see [16], [17], [18], and [19]), there have already been a great deal of successful participatory sensing campaigns in both civilian (see [20]) and military (see [21]) applications. Some of these campaigns utilize a centralized control mechanism while others are completely decentralized in nature.…”

Section: Participatory Sensing Campaignsmentioning

confidence: 99%

Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

2012

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Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e.g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload.In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e.g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems.

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A multi-agent infrastructure for hard and soft information fusion

Cited by 5 publications

References 23 publications

Foundational Technologies for Activity-Based Intelligence - A Review of the Literature

Foundational Technologies for Activity-Based Intelligence - A Review of the Literature

Conserving analyst attention units: use of multi-agent software and CEP methods to assist information analysis

Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

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