Multi-level Fusion of Hard and Soft Information for Intelligence

Biermann, Joachim; García, Juan Francisco Blanco; Krenc, Ksawery; Nimier, Vincent; Rein, Kellyn; Snidaro, Lauro

doi:10.1007/978-3-319-28971-7_17

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…In our previous works [4] we have proposed solutions applicable to systems which were communicating with usage of BML. In these cases information certainty has been modelled as a function of Credibility, Reliability, and Commonness, as (1) shows.…”

Section: Uncertainty (Formal Descriptions)mentioning

confidence: 99%

Challenges in automated HUMINT processing for situational assessment: Experiences from NATO CIMIC Joint Cooperation

Nimier

Rein

Snidaro

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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The role of the military has been continuously evolving and expanding beyond traditional warfare. In addition to an increasingly asynchronous battlefield, today's military is involved in supporting peacekeeping, crisis management, and disaster relief, to name just a few. Such activities require intense contact with diverse non-military organizations such as government, police, emergency services, relief agencies, and religious leaders. As a result, the information needed for commanders and other decision-makers must come from a wide variety of sources in a wide variety of formats. Thus, any attempt at fusion of this information requires an underlying system concept that is able to standardize information from a variety of input sources, including device-derived information (sensors) and (multiple) natural language(s), as well as to understand complex context, both static and dynamic, and to be able to deal with measures of uncertainty which may vary from source-type to source-type. This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (which have focused on the changing data and information incorporating structured and unstructured human generated information with device-generated data needs for fusion in increasingly complex scenarios requiring interaction betweendevices, human-generated information and complex contextual backgrounds. We discuss the differing challenges facing the synergic information processing as scenario complexity grows.

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Section: Uncertainty (Formal Descriptions)mentioning

confidence: 99%

Challenges in automated HUMINT processing for situational assessment: Experiences from NATO CIMIC Joint Cooperation

Nimier

Rein

Snidaro

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

Self Cite

View full text Add to dashboard Cite

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“…The vast number of possible sources and the heterogeneity of the data generated in a crisis scenario calls for some form of structured data already generated at sensor/platform level. Structured languages able to couch both soft data and hard data such as Controlled English and Battle Management Language (BML) (Biermann et al 2014) should be considered as pre-processing step; • Uncertainty of observations: this is one of the strongest points in favour of a logic-probabilistic relational reasoning model such as MLN. While retaining the expressiveness of FOL, MLN allows and handles contradicting observations and configurations of the values of the variables (ground atoms) that violate the satisfiability of the KB.…”

Section: Constraintsmentioning

confidence: 99%

Network Methods and Plan Recognition for Fusion in Crisis Management

Snidaro

Visentini

2016

Fusion Methodologies in Crisis Management

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Building and updating a situational picture of the scenario under consideration is the goal of the Situation Assessment (SA) Information Fusion (IF) process. The scenario generally involves multiple entities and actors where possibly only a few under direct control of the decision maker. SA aims at explaining the observed events (mainly) by establishing the entities and actors involved, inferring their goals, understanding the relations existing (whether permanently or temporarily) between them, the surrounding environment, and past and present events. It is therefore apparent how the SA process inherently hinges on understanding and reasoning about relations. SA is a necessary preparatory step to the following phase of Impact Assessment (IA) where the decision maker is interested in estimating the evolution of the situation and the possible outcomes, dangers and threats. SA and IA processes are particularly complex and critical for large-scale scenarios with nearly chaotic dynamics such as those affected by natural or man-made disasters. This chapter will discuss recent developments in information fusion methods for representing and reasoning about relational information and knowledge for event detection in the context of crisis management. In particular, network methods will be analysed as a means for representing and reasoning about relational knowledge with the purpose of detecting complex events or discovering the causes of observed evidence.

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Multi-level Fusion of Hard and Soft Information for Intelligence

Biermann

García

Krenc³

et al. 2016

Context-Enhanced Information Fusion

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Driven by the underlying need for an as yet undeveloped framework for fusing heterogeneous data and information at different semantic levels coming from both sensory and human sources, we present some results of the research conducted within the NATO Research Task Group IST-106/RTG-051 on “Information Filtering and Multi Source Information Fusion.” As part of this ongoing effort, we discuss here a first outcome of our investigation on multi-level fusion. It deals with removing the first hurdle between data/information sources and processes being at different levels: representation. Our contention here is that a common representation and description framework is the premise for enabling processing overarching different semantic levels. To this end, we discuss here the use of the Battle Management Language (BML) as a way (“lingua franca”) to encode sensor- and text-based data and a priori and contextual knowledge, both as hard and soft data. We here expand on our previous works [1, 2] further detailing and exemplifying the use of BML and clarifying aspects related to the use of contextual information and the exploitation of uncertain soft input along with sensor readings

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Multi-level Fusion of Hard and Soft Information for Intelligence

Cited by 7 publications

References 11 publications

Challenges in automated HUMINT processing for situational assessment: Experiences from NATO CIMIC Joint Cooperation

Challenges in automated HUMINT processing for situational assessment: Experiences from NATO CIMIC Joint Cooperation

Network Methods and Plan Recognition for Fusion in Crisis Management

Multi-level Fusion of Hard and Soft Information for Intelligence

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