This paper presents an effective pipeline for generating cross-domain plan options for Multi-Domain Operations through the coordinated use of legacy planning systems. We accomplish this by combining the intuitive interface of our Distributed InteRactivE C2 Tool (DIRECT) with the efficient distributed AI reasoning of our Multi-Domain Adaptive Request Service (MARS). Existing military planning processes are typically stove-piped and use a variety of manual and domain-specific legacy planning tools. This makes it challenging to generate a plan that draws on resources from different domains for different tasks, resulting in underutilized resources and unfulfilled tasks. MARS uses a marketplace approach to address this problem by conducting auctions that enable legacy planners to participate in a cross-domain planning process. Moreover, MARS provides a framework for developing new automated domain planners in cases with limited existing automated planning support. The process works for either pre-execution or in-execution planning. DIRECT provides an interface to the operator to navigate and compare these cross-domain plan options in the context of the original plan to provide transparency and build trust with the operator. Our experiments demonstrate the efficacy of the approach using a combination of our Air Planner and surrogate legacy planners for space and ground fires, as compared to stove-piped systems. For example, we are able to increase the percentage of requests fulfilled for sequencing strike and battle damage assessment effects from 38% using only single domain air assets to 54% when using the auction to draw in air, land, and space assets.
This paper presents how the combination of our Distributed InteRactivE C2 Tool (DIRECT) and Multi-Domain Adaptive Request Service (MARS) exploits underutilized resources through distributed adaptation of plans across domains. Deliberate planning processes, especially in the military, tend to be slow and unresponsive. Moreover, the introduction of more flexible assets such as multi-role aircraft introduces latent capacity that is often not exploited due to lack of flexible planning processes, thereby representing a significant opportunity to revolutionize the current system. We seek to overcome these challenges by enabling planners to respond to new requests during execution, through a semi-automated, distributed process that quickly generates options for adapting plans while meeting existing commitments, and presents them for human review. To accomplish this, we infer task state from reported mission states to simplify the manual process of tracking tasks and ensure that the adapted plan incorporates incomplete tasks but does not replan completed tasks. Our dynamic replanner generates options quickly, e.g., 316 seconds to adapt a plan with 345 missions to incorporate 1000 new tasks. This significantly increases utilization of resources, with 60%-70% of imagery requests for battle damage assessment being satisfied by multi-role fighters already flying. Finally, we provide options in context of the existing plan through adaptive option ranking that promotes options that meet operator preferences as judged from abstract evaluation factors designed to apply across different domains. The ranking achieves 80% accuracy for predicting the top option, presenting the preferred option to the operator the vast majority of the time.
Our Multi-INT Data Association Tool (MIDAT) learns patterns of life (POL) of a geographical area from video analyst observations called out in textual reporting. Typical approaches to learning POLs from video make use of computer vision algorithms to extract locations in space and time of various activities. Such approaches are subject to the detection and tracking performance of the video processing algorithms. Numerous examples of human analysts monitoring live video streams annotating or “calling out” relevant entities and activities exist, such as security analysis, crime-scene forensics, news reports, and sports commentary. This user description typically corresponds with textual capture, such as chat. Although the purpose of these text products is primarily to describe events as they happen, organizations typically archive the reports for extended periods. This archive provides a basis to build POLs. Such POLs are useful for diagnosis to assess activities in an area based on historical context, and for consumers of products, who gain an understanding of historical patterns. MIDAT combines natural language processing, multi-hypothesis tracking, and Multi-INT Activity Pattern Learning and Exploitation (MAPLE) technologies in an end-to-end lab prototype that processes textual products produced by video analysts, infers POLs, and highlights anomalies relative to those POLs with links to “tracks" of related activities performed by the same entity. MIDAT technologies perform well, achieving, for example, a 90% F1-value on extracting activities from the textual reports.
KEYWORDS: Sensors, Control systems, Detection and tracking algorithms, Radar, Computer simulations, Computer programming, Algorithm development, Monte Carlo methods, Data modeling, Stochastic processes
We consider the sensor management problem arising in air-to-ground tracking of moving targets. The sensing-tracking system includes a radar and a feature-aided tracker. The radar collects target-signature data in high-resolution-radar (HRR) mode. The tracker is using the collected HRR-signature data to create and maintain target-track identification information. More specifically, the tracker is learning target-track profiles from the collected signature data, and is using these profiles to resolve the potential report-to-track or track-to-track association ambiguities. In this paper, we focus on
the management of the HRR-signature data collection. Specifically, the sensor management problem is to determine where to collect signature data on targets in time so as to optimize the utility of the collected data. As with other sensor management problems, determining the optimal data collection is a hard combinatorial problem due to many factors including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics stems in part from the presence of the sensor slew time. A distinguishing feature of the sensor management problem considered here is that the HRR-signature data collected during the learning phase has no immediate value. To optimize the data collections, a sensor manager must look sufficiently far into the future to adequately trade-off alternative plans. Here, we propose some farsighted algorithms, and evaluate them against a sequential scanning and a greedy algorithm. We present our simulation results obtained by applying these algorithms to a problem of managing a single sensor providing HRR-signature data.
This paper presents a novel, scalable optimization approach to coordinating multiple sensor resources to track and
discriminate targets. The sensor resource management problem is one of allocating sensors on a time scale of seconds
in a closed loop that includes a target tracker and discrimination system. Allocations specify how the sensors should be
pointed and the modes in which they should operate. The objective is to optimize the collection of data to meet tracking
and discrimination goals. Quality of the data collected will be different for different allocations, in part, because the
quality of the data collected on a target by a sensor depends on the relative geometry between the sensor and target. The
optimization of the data collection is to be done subject to constraints on sensors' fields of view as well as time required
to slew a sensor into position, if it has mechanical gimbals, and to collect data in the selected mode. The problem is
challenging because of the large number of possible sensor allocations as well as the complex dynamics. For this
problem, we have developed a novel, approximate dynamic programming algorithm, a type of rollout algorithm, to
optimize sensor allocations to coordinate multiple sensor resources. The approach is scalable to realistically sized
problems. The paper overviews the approach and results from applying the algorithm.
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