N252-089 TITLE: Virtual Agent for Data Fusion and Understanding

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy

OBJECTIVE: Conceive and develop a novel artificial intelligence (AI) system—virtual agent—capable of sophisticated data fusion and comprehension, adapted for the Navy’s diverse data ecosystem. This virtual agent will be competent to process, explain, and generate actionable intelligence from heterogeneous data sources; thereby augmenting situational awareness and decision-making acumen within the dynamic maritime theater.

DESCRIPTION: The Navy is inundated with data emanating from myriad multiform sources: sensor data from maritime and aerial platforms, intelligence dossiers, maintenance logs, environmental metrics, communications intercepts, and so forth. Human analysts are presented with complex radar and sonar returns mapping physical spaces and threats, detailed textual and visual intelligence reports necessitating advanced linguistic and visual analytics, operational and maintenance data indicative of asset readiness, and critical oceanographic and meteorological data conditioning strategic operations.

The colossal volume and diversity of these data pose considerable challenges in terms of real-time processing, comprehensive understanding, and the distillation of actionable intelligence. Prevailing approaches anchored in generative AI have exhibited only limited success in natural language and image production and are ultimately wrecked on the shoals of complexity that human-level/human-style intelligence uniquely can comprehend. Generative AI models are constitutionally defective by their inability to explain—and not merely predict—patterns in data, which in addition disables them from generalizing across disparate data types.

To transcend these limitations, a paradigm shift towards a hybrid AI approach—synergizing human-style machine learning (ML) with human-style symbolic AI into a neurosymbolic hybrid—is imperative. Symbolic AI, using knowledge graphs, ontologies, and rule-based systems, can endow the virtual agent with domain knowledge and reasoning faculties. This consilience of ML and symbolic AI will empower the agent to integrate and interpret a wide spectrum of data, discern explicit causality from latent correlations, and generate robust, actionable insights. The envisioned virtual agent must thus leverage both advanced non-Large Language Models (non-LLM) ML and symbolic AI to actualize comprehensive data fusion and deep understanding.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

PHASE I: Conceive and develop a neurosymbolic hybrid virtual agent. This initial phase comprises the architectural design of the system and the selection of data types (including, but not limited to radar and sonobuoy data). The objective is to construct a nascent model that validates the feasibility of integrating these diverse data streams and generating preliminary insights. Additionally, this phase will encompass the establishment of evaluative metrics of system performance and the formulation of a scalable development plan for subsequent phases. Reference sources to determine trust/confidence in the system will include external validated knowledgebases and domain-specific training.

The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Build upon the foundational insights of Phase I, advance toward the comprehensive development, rigorous testing, and empirical validation of the virtual agent. Build and refine the algorithms and models, integrate additional data sources, and enhance the prototype system’s real-time processing capabilities. Test the virtual agent within realistic Navy operational scenarios to assess its efficacy in terms of accuracy, alacrity, and resilience. Demonstrate the agent’s prowess in delivering holistic situational awareness, anticipating potential threats, and proffering actionable strategic recommendations. Ensure that the virtual agent is a deployable system that substantially augments the Navy’s data fusion and understanding capabilities, thereby elevating operational efficacy and strategic decision-making.

Work in Phase II may become classified. Please see note in Description paragraph.

PHASE III DUAL USE APPLICATIONS: Complete final testing. Perform necessary integration and transition for use in operational applications with appropriate platforms and agencies, and future combat systems under development.

Commercially, this product could be used to enable security monitoring, smart city operations center, power grid monitoring, and wherever large amounts of sensors or inputs are utilized.

REFERENCES:

1. Garcez, A. D. A.; Gori, M.; Lamb, L. C.; Serafini, L.; Spranger, M. and Tran, S. N. "Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning." Journal of Applied Logics — IfCoLog Journal of Logics and their Applications, 2019. https://arxiv.org/pdf/1905.06088
2. Hoy, M. B. "Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants." Medical Reference Services Quarterly, 37(1), 2018, pp.81-88. https://pubmed.ncbi.nlm.nih.gov/29327988/
3. Marcus, G. and Davis, E. "Rebooting AI: Building artificial intelligence we can trust.". Pantheon Books, New York, 2019. https://search.worldcat.org/formats-editions/1083223029
4. "National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. 1993". https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004

KEYWORDS: Data fusion; artificial intelligence; machine learning; AI/ML; virtual agent; neurosymbolic agent; decision-making; symbolic AI

TPOC 1: Anthony Brescia
(301) 342-2094
[email protected]

TPOC 2: Charles Rea
(301) 342-9113
[email protected]

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