Dr. Laura Barbulescu Profile

Dr. Laura Barbulescu

at Carnegie Mellon Univ

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Publications (2)

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Proceedings Article | 6 June 2022 Presentation + Paper

Distributed, multi-domain option generation across legacy planners

M. Schneider, L. Barbulescu, L. Batlle-Rafferty, L. Smith, M. Cook, T. Kapler, M. Loppie, E. Pelletier, Z. Rubinstein, S. Smith, M. Miller, J. HandUber, C. Richard, G. Kuperman

Proceedings Volume 12113, 1211305 (2022) https://doi.org/10.1117/12.2618123

KEYWORDS: Visualization, Satellites, Weapons, Sensors, Satellite imaging, Transparency, Systems modeling, Surveillance, Distributed computing, Optimization (mathematics)

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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.

Proceedings Article | 12 April 2021 Presentation + Paper

Context-sensitive, distributed, multi-domain adaptive option generation

M. Schneider, L. Barbulescu, L. Batlle-Rafferty, M. Cook, T. Kapler, M. Loppie, E. Pelletier, Z. Rubinstein, . Sexton, S. Smith, D. Javorsek

Proceedings Volume 11746, 1174608 (2021) https://doi.org/10.1117/12.2589944

KEYWORDS: Mars

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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.

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