This basic research project focused on the specification of a top-level functional design of a new mixed-initiative approach providing effective and innovative computational support strategies that efficiently exploit human cognition while minimizing cognitive workload for achieving new intelligence analysis and decision-support capabilities. Toward improving analysis capabilities, we build in part on the similarly-oriented works of researchers that have argumentation methods at the core of their strategies for providing computationally-based support for analysis. However, in our approach, a central theme combines the story- and argumentation-based methods following suggestions in the literature into a hybrid scheme. The argumentation-based foundation provides the advantages of: 1) a basis on simple principles of reasoning, 2) explication of the generalizations and the evidence in the arguments, and 3) allowing the reasoning from the evidence to a conclusion to be easy to follow. In framing our overall functional analysis and decision-support architecture, we also leverage our own research in topic modeling for computational support to narrative development, and in methods for hard and soft data association, fusion, and inferencing. Our approach also takes an Open-World approach and as well addresses the issue of uncertainty in a mathematically rigorous way using a technique called the Transferable Belief Model (TBM). This paper focuses on the highlights of this overall approach; extended details are provided in our citations.
Lockheed Martin Advanced Technology Laboratories (LM ATL) is researching methods, representations, and processes for human/autonomy collaboration to scale analysis and hypotheses substantiation for intelligence analysts. This research establishes a machinereadable hypothesis representation that is commonsensical to the human analyst. The representation unifies context between the human and computer, enabling autonomy in the form of analytic software, to support the analyst through proactively acquiring, assessing, and organizing high-value information that is needed to inform and substantiate hypotheses.
Lockheed Martin Advanced Technology Laboratories (LM ATL) is collaborating with
Professor James Llinas, Ph.D., of the Center for Multisource Information Fusion at
the University at Buffalo (State of NY), researching concepts for a mixed-initiative
associate system for intelligence analysts to facilitate reduced analysis and decision
times while proactively discovering and presenting relevant information based on
the analyst’s needs, current tasks and cognitive state. Today’s exploitation and
analysis systems have largely been designed for a specific sensor, data type, and
operational context, leading to difficulty in directly supporting the analyst’s evolving
tasking and work product development preferences across complex Operational
Environments. Our interactions with analysts illuminate the need to impact the
information fusion, exploitation, and analysis capabilities in a variety of ways,
including understanding data options, algorithm composition, hypothesis validation,
and work product development. Composable Analytic Systems, an analyst-driven
system that increases flexibility and capability to effectively utilize Multi-INT fusion
and analytics tailored to the analyst’s mission needs, holds promise to addresses the
current and future intelligence analysis needs, as US forces engage threats in
contested and denied environments.
As military tactics evolve toward execution centric operations the ability to analyze vast amounts of mission relevant
data is essential to command and control decision making. To maintain operational tempo and achieve information
superiority we have developed Vigilant Advisor, a mobile agent-based distributed Plan Execution Monitoring system.
It provides military commanders with continuous contingency monitoring tailored to their preferences while
overcoming the network bandwidth problem often associated with traditional remote data querying. This paper presents
an overview of Plan Execution Monitoring as well as a detailed view of the Vigilant Advisor system including key
features and statistical analysis of resource savings provided by its mobile agent-based approach.
Transformation of military information systems to a network-centric paradigm will remove traditional barriers to
interoperability and enable dynamic access to information and analysis resources. The technical challenges of
accomplishing network-centric warfare (NCW) require the engineering of agile distributed software components imbued
with the ability to operate autonomously on behalf of human individuals, while maintaining system level integrity,
security, and performance efficiency on a grand scale.
In this paper, we will describe how agents provide a critical technology enabler for applying emerging commercial
technologies, such as web services, into network-centric warfare problems. The objective of our research is developing
and sharing battlespace awareness and understanding. Our agent information service manages information collection and
dissemination/publishing activities on behalf of fusion services in an autonomous, yet controllable fashion. Agents
improve the scalability and reliability at the system of systems level through dynamic selection and exploitation of web
services based upon needs and capabilities.
Conference Committee Involvement (2)
Next-Generation Analyst VI
16 April 2018 | Orlando, Florida, United States
Next-Generation Analyst V
10 April 2017 | Anaheim, California, United States