Diego Fernando Martinez Ayala

Optimization-Based Decision Support Software for a Team-in-the-Loop Experiment: Multilevel Asset Allocation

Motivated by the Navys interest in decision support tools that augment planning activities within a maritime operations center (MOC), we have developed a multilevel resource allocation model that is capable of interacting with human planners to dynamically allocate hierarchically-organized assets to process interdependent tasks in order to accomplish mission objectives. The planning problem is formulated as a mixed-integer nonlinear programming (MINLP) problem of minimizing the overall difference between the human-specified desired task accuracy performance criteria and the expected performance outcomes, the latter being based on how well the assigned resources match the required resources, subject to a number of real-world planning constraints. To solve the resulting large-scale MINLP problem, we propose two methods: 1) a Lagrangian relaxation method that solves the multilevel asset allocation problem with a measure of sub-optimality in terms of an approximate duality gap and 2) a dynamic list planning heuristic algorithm that provides high-quality sub-optimal solutions rapidly (less than 10 s for the scenarios considered here). Finally, we verify our methods using realistic MOC planning scenarios, provide a comparative evaluation of the performance measures of the two proposed methods, and investigate the value of information via human-in-the-loop experiments.

Context-based models to overcome operational challenges in maritime security

Piracy and smuggling are major international problems which not only threaten maritime security but also affect the global economy. Even though NATO and international forces have been relentlessly fighting maritime crime in East Africa (Gulf of Aden), the problems still persist and maritime crime has moved to West Africa (Gulf of Guinea). In the same vein, the Joint Interagency Task Force-South (JIATF-S) has had substantial operational success, controlling the vast geographical spread of smugglers in the East Pacific and Caribbean Sea. Due to limited number of maritime assets available, every resource needs to be efficiently allocated, both in time and space. This poses a great operational challenge requiring the integration and fusion of disparate information relevant to the mission and dynamic allocation of resources under uncertainty. Operational planning and execution for counter-smuggling and counter-piracy operations involve surveillance (to search, detect, track and identify potential threats) and interdiction operations (to intercept, investigate and potentially apprehend the suspects) in a dynamic and uncertain mission environment. In this paper, we present context-based models for counter-smuggling and counter-piracy missions, where the smuggling and piracy activities are represented in the form of color coded heat maps built using Intelligence information (INTEL), meteorological and oceanographic (METOC) information and other mission-specific attributes (sensor observations, target types and their behavior, etc.); these maps are interpreted as probability of activity (PoA) surfaces. These PoA surfaces form an input to the decision support module discussed in this paper.

Decision support software for Anti-Submarine warfare mission planning within a dynamic environmental context

Anti-Submarine Warfare (ASW) involves effective allocation and path planning of ASW platforms to search for, detect, classify, track and prosecute hostile submarines within an evolving environment. As the environmental context evolves rapidly, continuously collected Meteorological and Oceanographic (METOC) data is used for assessing the impact of the current and forecasted environment on individual sensors and weapon platforms, as well as on tactics in the form of performance surfaces, which is presented to the commanders in making go/no-go decisions. However, due to the overwhelming amount of METOC information, it is very challenging for the commanders to interpret and analyze the data for generating plans or evaluating courses of action in a timely manner. In this paper, motivated by the need to assist ASW commanders in making proactive decisions in an evolving environmental context, we present a decision support tool for modeling and incorporating the appropriate METOC information from multiple sources and further utilizing it to determine the search regions and optimal trajectories to search for and track the enemy submarines in a timely manner.

Decision support information integration platform for context-driven interdiction operations in counter-smuggling missions

Context-driven decision making is at the top of the Navys agenda of important concepts to be embedded in future proactive decision support systems for command decision making. There are manifold challenges associated with relaying contextual data in a timely manner to the decision maker. In the counter-smuggling domain, for example, although high value information is accessible, it is dispersed across databases and the decision making team. There are numerous research challenges in integrating this information in an efficient manner to effectively present viable courses of action to a decision making team. In this paper, we propose a decision support tool for counter-smuggling missions modeled as a stochastic control problem of dynamically managing assets to maximize the probability of detecting and interdicting maritime illicit trafficking operations. We additionally propose a method to explain the algorithm behavior to the human decision maker and provide them with interactive controls to develop “what-if” solutions or to constrain solutions to a desired path.

Context-Aware Dynamic Asset Allocation for Maritime Interdiction Operations

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force—South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans.

Markov Modeling and Analysis of Team Communication

This paper presents a predictive data analytics process for examining the relationship between team communication and performance in planning tasks. Team performance is measured in terms of the time each team spends in completing the planning task and the cost of the concomitant work schedule. The predictive data analytics process encompasses three data abstraction techniques for data preparation, three probabilistic models that represent the temporal features of data abstracted from team communication interactions, and a validation process that selects the best pair of data abstraction and model for subsequent insight analysis. Experimental data obtained from 32 teams of three members each, tasked to solve a personnel scheduling problem, is used for validating the proposed methodology.

Context-Aware Decision Support for Anti-Submarine Warfare Mission Planning Within a Dynamic Environment

Anti-submarine warfare (ASW) missions are the linchpin of maritime operations involving effective allocation and path planning of scarce assets to search for, detect, classify, track, and prosecute hostile submarines within a dynamic and uncertain mission environment. Motivated by the need to assist ASW commanders to make better decisions within an evolving mission context, we investigate a moving target search problem with multiple searchers and develop a context-driven decision support tool for the ASW mission planning problem. Given the spatial probability distribution of a target submarine, sensor detection probability surfaces from meteorological and oceanographic products, and the risk to the fleet as a function of distance of the target from the fleet, we model and formulate the ASW asset allocation and search path planning problem using a hidden Markov modeling framework. We propose a two phase approach to solve this NP-hard problem. In phase I, we partition the geographic area, satisfying contiguity constraints, into search regions using an evolutionary algorithm (EA) coupled with a Voronoi tessellation approach, and allocate the assets to partitioned search areas using the auction algorithm. In phase II, we construct a dynamic search plan for each asset over the search interval using EA. We evaluate our approach via a hypothetical ASW scenario to monitor an enemy submarine in a geographic region via multiple assets. We compare our results to various search path planning strategies that, using the context-driven decision support tool developed here, revise the search regions at periodic intervals given a fixed total search time.

A Context-Driven Framework for Proactive Decision Support With Applications

Major challenges anticipated in the future C4ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance) operations involve rapid mission planning/ re-planning in highly dynamic, asymmetric, unpredictable, and network-centric environments. Developing decision support for such complex mission environments requires automated processing, interpretation, and development of proactive decisions using large volumes of structured, unstructured, and semi-structured data, while simultaneously decreasing the time necessary to arrive at a decision. To overcome this data deluge, there is a need for mastering information dominance via acquisition, fusion, and transfer of the right data/information/knowledge from the right sources in the right mission context to the right decision-maker (DM) at the right time for the right purpose (6R). The fundamental challenge in achieving the 6R is to conceive a generic framework that encompasses the dynamics of relevant contextual elements, their interdependence and correlation to the current and evolving situation, while taking into account the cognitive status of the DM. In this paper, we propose a context-driven proactive decision support (PDS) framework that comprises: 1) adaptive model-based dynamic graph models (e.g., Dynamic Hierarchical Bayesian Networks) and the concomitant inference algorithms for context representation, inference, and forecasting, 2) information selection, valuation, and prioritization methods for context-driven operations, including uncertainty management approaches, and 3) application of PDS concepts for courses of action recommendations across representative maritime operations.