Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don’t push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas:
Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development.
Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns.
Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.
Synchronization of Intelligence, Surveillance, and Reconnaissance (ISR) activities to maximize the utilization of limited resources (both in terms of quantity and capability) has become critically important to military forces. In centralized frameworks, a single node is responsible for determining and disseminating decisions (e.g., tasks assignments) to all nodes in the network. This requires a robust and reliable communication network. In decentralized frameworks, processing of information and decision making occur at different nodes in the network, reducing the communication requirements. This research studies the degradation of solution quality (i.e., potential information gain) as a centralized system synchronizing ISR activities moves to a decentralized framework. The mathematical programming model of previous work1 has been extended for multi-perspective optimization in which each collection asset develops its own decisions to support mission objectives based only on its perspective of the environment. Different communication strategy are considered. Collection assets are part of the same communication network (i.e., a connected component) if: (1) a fully connected network exists between the assets in the connected component, or (2) a path (consisting of one or more communication links) between every asset in the connected component exists. Multiple connected components may exist among the available collection assets supporting a mission. Information is only exchanged when assets are part of the same network. The potential location of assets that are not part of a connected component can be considered (with a suitable decay factor as a function of time) as part of the optimization model.
One of the main technical challenges facing intelligence analysts today is effectively determining information gaps from huge amounts of collected data. Moreover, getting the right information to/from the right person (e.g., analyst, warfighter on the edge) at the right time in a distributed environment has been elusive to our military forces. Synchronization of Intelligence, Surveillance, and Reconnaissance (ISR) activities to maximize the efficient utilization of limited resources (both in quantity and capabilities) has become critically important to increase the accuracy and timeliness of overall information gain. Given this reality, we are interested in quantifying the degradation of solution quality (i.e., information gain) as a centralized system synchronizing ISR activities (from information gap identification to information collection and dissemination) moves to a more decentralized framework. This evaluation extends the concept of price of anarchy, a measure of the inefficiency of a system when agents maximize decisions without coordination, by considering different levels of decentralization. Our initial research representing the potential information gain in geospatial and time discretized spaces is presented. This potential information gain map can represent a consolidation of Intelligence Preparation of the Battlefield products as input to automated ISR synchronization tools. Using the coordination of unmanned vehicles (UxVs) as an example, we developed a mathematical programming model for multi-perspective optimization in which each UxV develops its own fight plan to support mission objectives based only on its perspective of the environment (i.e., potential information gain map). Information is only exchanged when UxVs are part of the same communication network.
This paper discusses how methods used for conventional multiple hypothesis tracking (MHT) can be extended to
domain-agnostic tracking of entities from non-kinematic constraints such as those imposed by cyber attacks in a
potentially dense false alarm background. MHT is widely recognized as the premier method to avoid corrupting tracks
with spurious data in the kinematic domain but it has not been extensively applied to other problem domains. The
traditional approach is to tightly couple track maintenance (prediction, gating, filtering, probabilistic pruning, and target
confirmation) with hypothesis management (clustering, incompatibility maintenance, hypothesis formation, and Nassociation
pruning). However, by separating the domain specific track maintenance portion from the domain agnostic
hypothesis management piece, we can begin to apply the wealth of knowledge gained from ground and air tracking
solutions to the cyber (and other) domains. These realizations led to the creation of Raytheon's Multiple Hypothesis
Extensible Tracking Architecture (MHETA).
In this paper, we showcase MHETA for the cyber domain, plugging in a well established method, CUBRC's
INFormation Engine for Real-time Decision making, (INFERD), for the association portion of the MHT. The result is a
CyberMHT. We demonstrate the power of MHETA-INFERD using simulated data. Using metrics from both the
tracking and cyber domains, we show that while no tracker is perfect, by applying MHETA-INFERD, advanced nonkinematic
tracks can be captured in an automated way, perform better than non-MHT approaches, and decrease analyst
response time to cyber threats.