Accurately mapping spatial phenomena with limited observations hinges on selecting sampling sites that minimize predictive uncertainty. We model this task by quantifying unsampled-location uncertainty with ordinary Kriging and framing site selection as an optimization problem. Because the resulting Kriging prediction-variance objective is nonlinear, we derive an integer program approximation called Kriging-informed coverage sampling that bounds the Kriging variance with a set of linear constraints. We prove that Kriging-informed coverage sampling is isomorphic to the classical Maximal Coverage Location Problem, thereby linking geostatistical uncertainty reduction to a well-studied family of location problems and enabling the use of well-established solution techniques. Computational experiments on synthetic landscapes and a remote-sensing case study show that Kriging-informed coverage sampling attains 90% of the information gain achieved by exact non-linear solution methods while reducing solution times by up to two orders of magnitude.

The kidney exchange problem (KEP) determines a set of planned kidney transplants, an exchange plan, for a pool of nondirected donors and biologically incompatible patient-donor pairs (PDPs) that maximizes weighted transplant quantity. Exchange pool members with fewer compatible donors experience bias, being excluded from exchange plans more often. To reduce bias, KEP optimization considers fairness at the group level, which obscures individual differences, or at the individual level, which is resource intensive. To bridge the gap between these approaches, we develop a model, individually fair outcomes for the KEP (IFO-KEP), which uses a novel prioritization schema, to individually prioritize PDPs, based on their probability of being included in fairness-agnostic optimization, within a hierarchical optimization approach balancing planned transplant quantity and fairness. Analysis compares IFO-KEP to existing individual and group fairness KEP approaches in myopic contexts, using metrics related to exchange performance and measures of parity, and dynamic contexts, evaluating exchange pool dynamics over time. In myopic contexts, IFO-KEP generates KEP solutions that achieve a high degree of parity without decreasing expected utility. In dynamic testing, IFO-KEP achieves fairness without causing negative impacts on long-term exchange performance. IFO-KEP is a novel method to achieve individually fair KEP exchange plans and reduce the bias against PDPs with low access to a compatible donor, without high-resource requirements. Global growth in KEP exchange pools elevates the importance of our contributions.

In many military situations, emplacing assets at the tactical edge with smart processing capabilities is cost and/or risk prohibitive. At the same time, as more and more sensors are deployed on assets to collect and/or generate data, efficiently routing that data through communication degraded or contested environments becomes extremely difficult. This research focuses on the determination of which nodes in a network should be smart, with the ability to process/fuse data that is routed through them, in order to reduce the overall amount of data flowing over the network as well as potentially increasing the value of the data flowing over the network.

Maritime fleet tracking is a critical piece of naval operations. Leveraging the inherent spatial and temporal autocorrelation of vessels in a fleet, we use spatio-temporal Kriging, an interpolation technique, to estimate the likelihood of finding a vessel at a specific location. This estimation is based solely on the current and/or past locations of. other vessels within the fleet. We do this by first fitting covariance models to observed fleet movements. We then use spatio-temporal indicator Kriging to forecast the locations of vessels in a fleet at different times, with or without new information. Our results indicate a notable improvement in accuracy, ranging from 60 to 90% compared to a baseline model. We measure accuracy using ROC AUC values. Furthermore, our study reveals that tracking only a subset of vessels within a fleet significantly enhances understanding of the entire fleet’s movements. However, the number of vessels that needs to be tracked increases as we move further from the last observation of the entire fleet. Future extensions of our work include integrating additional situational information, using other spatio-temporal interpolation techniques, and expanding its application beyond maritime fleets.

The Kidney Exchange Problem (KEP) determines organ exchange chains and cycles amongst a pool of patient-donor pairs (PDP) and non-directed donors (NDD) allowing for the maximum number of kidney transplants. The problem is complicated by optimization occurring over a sparsely connected, directed graph. The presence of an edge in this graph suggests a feasible transplant from a NDD or PDP to another PDP. Many traditional approaches treat the presence of edges in the exchange pool as known and certain. However, the certainty of edges in the exchange is unknown until optimization has been completed and transplants are offered. Edges that are thought to be present may fail because of physician preference, patient behavior, or previously unknown biological incompatibility. As a result, a disparity exists between the number of exchanges planned in optimal solutions and the number of exchanges that take place in the real world. Therefore, this work proposes an integrated KEP optimization methodology that learns a representation of features that affect the realization of optimized solutions. This methodology uses graph machine learning and allows for the integration of additional patient-donor attributes and collaboration between the optimization process and physician behavior. To evaluate this solution method an approach for simulating the implementation of KEP solutions is developed. An analysis of the required data inputs for both the solving and assessment methodology is noted and the potential benefits of the framework are described. A discussion of the limitations of the work is presented and directions for future works are proposed.

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In this work, we present an attention-based encoder-decoder model to approximately solve the team orienteering problem with multiple depots (TOPMD). The TOPMD instance is an NP-hard combinatorial optimization problem that involves multiple agents (or autonomous vehicles) and not purely Euclidean (straight line distance) graph edge weights. In addition, to avoid tedious computations on dataset creation, we provide an approach to generate synthetic data on the fly for effectively training the model. Furthermore, to evaluate our proposed model, we conduct two experimental studies on the multi-agent reconnaissance mission planning problem formulated as TOPMD. First, we characterize the model based on the training configurations to understand the scalability of the proposed approach to unseen configurations. Second, we evaluate the solution quality of the model against several baselines-heuristics, competing machine learning (ML), and exact approaches, on several reconnaissance scenarios. The experimental results indicate that training the model with a maximum number of agents, a moderate number of targets (or nodes to visit), and moderate travel length, performs well across a variety of conditions. Furthermore, the results also reveal that the proposed approach offers a more tractable and higher quality (or competitive) solution in comparison with existing attention-based models, stochastic heuristic approach, and standard mixed-integer programming solver under the given experimental conditions. Finally, the different experimental evaluations reveal that the proposed data generation approach for training the model is highly effective.

To increase efficiency in all-domain military operations, such as achieving desired effects within severely compressed decision cycles, a multi-prong approach to technology is required. Traditional approaches provide a single algorithmic solution that keeps users, and acquisition efforts, “locked-in” to a result that may not be ideal for a particular mission, or possibly hinders technological advancements. Combining algorithms through ‘plug and play’, users can select the optimal algorithm(s) for their mission, and the acquisition community can easily improve existing or introduce new algorithms, thereby increasing performance while reducing cost. Our solution provides a set of heterogeneous independent optimization algorithms (IOAs) developed separately by three defense contractors, coordinated by a central Meta- Optimizer (MO) that is connected to a simulation and testing (S&T) environment.

Entity resolution is an important data association task when fusing information from multiple sources. Oftentimes the information arrives continuously and the entity resolution algorithm needs to efficiently update its solution upon receiving new information. In this work, we introduce an incremental entity resolution algorithm based on a graph partitioning formulation. The developed algorithm is able to handle both incrementally arriving entity references, as well as incrementally arriving information which changes the pairwise similarity scores between the references. New information is handled in a way that allows the algorithm to reconsider past decisions when contradicting information arrives. Because the graph partitioning formulation used is NP-Hard, a heuristic algorithm is developed to produce good solutions, which is also compatible with a blocking technique to limit the number of required comparisons. The algorithm is tested on a variety of datasets (randomly generated and real) and it is shown that allowing the algorithm to consider revised scores and revisit prior decisions offers a substantial improvement to accuracy (approximately 30-40% better F-Score on a natural language dataset), compared to other greedy heuristics on the same set of coefficients. It is also shown that, on a test set with 100 references, the incremental algorithm is up to an order of magnitude faster than a batch algorithm approach that re-solves the entire problem.

We study the convoy movement problem in peacetime from a civilian perspective by seeking to minimize civilian traffic disruptions. We develop an exact hybrid algorithm that combines the k-shortest path algorithm along with finding a minimum weighted k-clique in a k-partite graph. Through this coupling scheme, we are able to exactly solve large instances of the convoy movement problem without relaxing many of its complicating constraints. An experimental study is performed based on pseudo-transportation networks to illustrate the computational viability of the method as well as policy implications.

Current decision making processes separate the intelligence tasks from the operations tasks. This creates a system that is reactive rather than proactive, leaving potential gains in the timeliness and quality of responding to a situation of interest. In this paper we will present a new optimization paradigm that combines the tasking of intelligence, surveillance, and reconnaissance (ISR) assets with the tasks and needs of operational assets. Some of the collection assets will be dedicated for one function or another, while a third category that could perform both will also be considered. We will use a scenario to demonstrate the value of the merger by presenting the impact to a number of intelligence and operations measures of performance and effectiveness (MOPS/MOES). Using this framework, mission readiness and execution assessment for a simulated humanitarian assistance/disaster relief (HADR) mission is monitored for tasks on intelligence gathering, distribution of supplies, and repair of vital lanes of transportation, during the relief effort. The results demonstrate a significant improvement to measures of performance when intelligence tasking takes operational objectives into consideration.

In this work, methods are developed to overcome the inherent problems of network abstraction and analysis from multiple heterogeneous data sources. RDF and attributed graphs are two common choices for graph modeling. While both are very similar with respect to the type of information that can be represented, characteristics intrinsic to each representation affect the analysis performed over the resultant network abstractions. By selecting a dual graph representation approach and leveraging the strengths of both models, the semantic analysis performed over RDF graphs is combined with the topological analysis applied to attributed graphs, to produce a comprehensive foundation for network analysis that cannot be easily achieved, nor its value matched, by one representation independently.

Roles and capabilities of analysts are changing as the volume of data grows. Open-source content is abundant and users are becoming increasingly dependent on automated capabilities to sift and correlate information. Entity resolution is one such capability. It is an algorithm that links entities using an arbitrary number of criteria (e.g., identifiers, attributes) from multiple sources. This paper demonstrates a prototype capability, which identifies enriched attributes of individuals stored across multiple sources. Here, the system first completes its processing on a cloud-computing cluster. Then, in a data explorer role, the analyst evaluates whether automated results are correct and whether attribute enrichment improves knowledge discovery.

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.

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 (UHC): 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.

Cyber networks are used extensively by not only a nation's military to protect sensitive information and execute missions, but also the primary infrastructure that provides services that enable modern conveniences such as education, potable water, electricity, natural gas, and financial transactions. Disruption of any of these services could have widespread impacts not only to citizens' well-being. As such, these critical services may be targeted by malicious hackers during cyber warfare. Due to the increasing dependence on computers for military and infrastructure purposes, it is imperative to not only protect them and mitigate any immediate or potential threats, but to also understand the current or potential impacts beyond the cyber networks or the organization. This increased dependence means that a cyber attack may not only affect the cyber network, but also other tasks or missions that are dependent upon the network for execution and completion. It is therefore necessary to try to understand the current and potential impacts of cyber effects on the overall mission of a nation's military, infrastructure, and other critical services. The understanding of the impact is primarily controlled by two processes: state estimation and impact assessment. State estimation is the process of determining the current state of the assets while impact assessment is the process of calculating impact based on the current asset states.

What is the problem under consideration? Why is government intervention necessary? The Department of Health intends to set-up a new NHS Commissioning Board or a similar body within DH that will be responsible for securing improved outcomes for NHS patients through the commissioning process. Under this "Principal-Agent" setting problems may arise if DH and the commissioning body have divergent objectives or if there is asymmetry of information about the quality of care provided.

Having dedicated the previous chapter to the second level of SA, we now proceed to the third level. The highest level of SA—projection—involves envisioning how the current situation may evolve into the future situation and the anticipation of the future elements of the situation. In the context of CSA, particularly important is the projection of future cyber attacks, or future phases of an ongoing cyber attack. Attacks often take a long time and involve multitudes of reconnaissance, exploitations, and obfuscation activities to achieve the goal of cyber espionage or sabotage. The anticipation of future attack actions is generally derived from the presently observed malicious activities. This chapter reviews the existing state-of-the-art techniques for network attack projection, and then explains how the estimates of ongoing attack strategies can then be used to provide a prediction of likely upcoming threats to critical assets of the network.