In this paper, we report on research underpinning CISpaces.org, a tool to support the process of sense-making, complementing human expertise in the generation of intelligence products. The model combines a structured, graphical representation of the analyst’s reasoning process with efficient artificial intelligence algorithms to automatically identify plausible hypotheses. Information extracted from open sources can be exploited in the sense-making process, and analysts may collaborate to bring different perspectives to the problem concerned. The provenance of both evidence used and analyses (co-)produced are recorded and may be used for further investigation, reporting and audit.
The methodology provides a rigorous means to record and support the process of forming hypotheses from the relationships among information. We use natural language processing algorithms to extract factual claims from open information sources. The core process of reasoning is made explicit in the structuring of evidence. Given this, we do not rely on the analyst exhaustively enumerating all possible hypotheses; we automate the identification of what evidence and claims together constitute a plausible interpretation of an analysis, enabling the analyst may explore all possibilities. As a further means to mitigate biases in human reasoning, we highlight critical questions that may undermine inferential assumptions of various kinds: Is there an alternative cause? Do other experts disagree? These structured models may then be used to automatically generate tailored reports to key decision makers as required, or as the situational understanding shifts.
A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although
probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, they do not provide
an explicit means for reasoning about second order uncertainty. Dempster-Shafer theory of evidence (DST) overcomes this
weakness and provides means to fuse and reason about uncertain information. In this paper, we combine DL-Lite with
DST to allow scalable reasoning over uncertain semantic knowledge bases. Furthermore, our formalism allows for the
detection of conflicts between the fused information and domain constraints. Finally, we propose methods to resolve such
conflicts through trust revision by exploiting evidence regarding the information sources. The effectiveness of the proposed
approaches is shown through simulations under various settings.
In modern coalition operations, decision makers must be capable of obtaining and fusing data from diverse
sources. The reliability of these sources can vary, and, in order to protect their interests, the data they provide
can be obfuscated. The trustworthiness of fused data depends on both the reliability of these sources and their
obfuscation strategy. Information consumers must determine how to evaluate trust in the presence of obfuscation,
while information providers must determine the appropriate level of obfuscation in order to ensure both that
they remain trusted, and do not reveal any private information. In this paper, through a coalition scenario, we
discuss and formalise trust and obfuscation in these contexts and the complex relationships between them.
Information derived from sensor networks plays a crucial role in the success of many critical tasks such as
surveillance, and border monitoring. In order to derive the correct information at the right time, sensor data
must be captured at desired locations with respect to the operational tasks in concern. Therefore, it is important
that at the planning stage of a mission, sensing resources are best placed in the field to capture the required
data. For example, consider a mission goal identify snipers, in an operational area before troops are deployed -
two acoustic arrays and a day-night video camera are needed to successfully achieve this goal. This is because,
if the resources are placed in correct locations, two acoustic arrays could provide direction of the shooter and
a possible location by triangulating acoustic data whereas the day-night camera could produce an affirmative
image of the perpetrators.
In order to deploy the sensing resources intelligently to support the user decisions, in this paper we propose a
Semantic Web based knowledge layer to identify the required resources in a sensor network and deploy the needed
resources through a sensor infrastructure. The knowledge layer captures crucial information such as resources
configurations, their intended use (e.g., two acoustic arrays deployed in a particular formation with day-night
camera are needed to identify perpetrators in a possible sniper attack). The underlying sensor infrastructure
will assists the process by exposing the information about deployed resources, resources in theatre, and location
information about tasks, resources and so on.
The net-centric ISR/ISTAR networks are expected to play a crucial role in the success of critical tasks such as
base perimeter protection, border patrol and so on. To accomplish these tasks in an effective and expedient
manner, it is important that these networks have the embedded capabilities to discover, delegate, and gather
relevant information in a timely and robust manner. In this paper, we present a system architecture and an
implementation that combines a service based reasoning mechanism with a sensor middleware infrastructure so
that tasks can be executed efficiently and effectively. A knowledge base, utilising the Semantic Web technologies,
provides the foundation for reasoning mechanism that assists users to discover, identify and allocate resources
that are made available through the middleware, in order to satisfy the needs of tasks. Once resources are
allocated to any given task, they can be accessed, controlled, shared, and their data feeds consumed through the
Fabric middleware. We use the semantic descriptions from the knowledge base to annotate the resources (types,
capabilities, etc.) in the sensor middleware so that they can be retrieved for reasoning during the discovery and
identification phases. The reasoner is implemented as a HTTP web service, with the following characteristics:
1. Computational intensive operations are off-loaded to dedicated nodes, preserving the resources in the
2. HTTP services are accessible through a standard set of APIs irrespective of the reasoner technology used.
3. Support for seamless integration of different reasoners into the system.
Data fusion plays a major role in assisting decision makers by providing them with an improved situational
awareness so that informed decisions could be made about the events that occur in the field. This involves
combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete,
dependable etc.) than what it would have been if the data streams (hereinafter referred to as 'feeds') from the
resources are taken individually. However, these feeds lack any context-related information (e.g., detected event,
event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating
an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources.
In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have
proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular
event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be
related to the current fusion. We use a subset of Web Ontology Language (OWL), OWL-DL to present a
lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts.
We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We
discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed
could assist analysts, planners to make better informed decisions.