Neuro-Symbolic Multi-Agent Factual AI™ Platform

What is the Neuro-Symbolic Multi-Agent Platform?

The Neuro-Symbolic Multi-Agent Platform is a collaborative AI system that supports complex decision-making for government agencies, finance, and other critical industries.

The Neuro-Symbolic Multi-Agent Platform integrates symbolic reasoning and neural learning to enhance operational planning and real-time situational analysis. This platform leverages multi-agent systems—a collaborative network of AI agents that analyze, simulate, and evaluate scenarios in tandem. It is built to handle high-stakes environments, providing users with transparent, explainable decision-making while ensuring scalability and adaptability across domains such as defense, finance, and government.

Platform Capabilities:

Neuro-Symbolic Reasoning: Merging the pattern recognition of neural networks with the logical structuring of symbolic AI, the platform allows agents to interpret complex scenarios with both flexibility and accuracy. The neuro-symbolic combination enhances the ability to break down tasks, solve them sequentially, and adapt to novel or unexpected conditions.
Multi-Agent Collaboration: The platform is designed to operate as a system of interconnected agents, each specialized in unique functions like scenario development, mission analysis, and outcome evaluation. This multi-agent structure enables parallel processing, reducing decision times and improving outcomes by examining scenarios from multiple perspectives simultaneously.
Dynamic Knowledge Graph Integration: Acting as the platform’s backbone, the knowledge graph is continuously updated to reflect new information, enabling agents to interact and ground their decisions in current, reliable data. This capability supports real-time contextual awareness, drawing from sources like structured databases and unstructured intelligence inputs.

Key Elements:

1. Explainable Decision-Making: With neuro-symbolic reasoning, the platform delivers decision outputs that are traceable and understandable, enhancing trust in automated processes. Each AI agent can explain its decision-making steps, providing transparency essential for sectors like defense and finance.
2. Adaptability Across Sectors: Though developed initially for defense, the platform’s flexibility allows it to support diverse applications, from financial modeling and risk assessment to government policy analysis. Its underlying framework accommodates the unique data and procedural needs of various industries without extensive reconfiguration.
3. Self-Improving through Human-AI Feedback: A continuous human-in-the-loop system enables feedback from domain experts, refining the platform’s accuracy over time. By learning from human interactions, the agents improve their understanding and decision-making to align more closely with real-world expectations.

Operational Use Cases

Financial Services

By integrating market data and predictive modeling, it supports investment analysis and risk management. The platform can predict market trends, flag emerging risks, and offer strategies based on comprehensive data and historical trends.

Portfolio Optimization: Assists asset managers in balancing risk and reward by simulating different investment strategies and their potential outcomes under varying market conditions.
Anomaly Detection: Identifies irregular market activities, such as sudden fluctuations or suspicious trades, helping detect potential fraud and mitigate financial loss.
Sentiment Analysis: Monitors and analyzes news and social media sentiment on key financial topics, allowing firms to adjust strategies based on public and investor sentiment shifts.
Regulatory Compliance: Helps maintain compliance by cross-referencing financial activities against regulatory guidelines, automatically flagging areas that require review.

Government Agency Applications

Government Policy Analysis: The platform enables policy analysts to dynamically simulate and evaluate the impact of policy changes, merging demographic, economic, and legislative data to forecast outcomes and assess risks.
Socioeconomic Forecasting: Provides scenario-based forecasts on economic or demographic shifts, enabling informed policy-making and resource allocation.
Public Health Policy Simulation: Evaluates potential impacts of health policies on population well-being, including disease spread, vaccination rates, and healthcare resource demand.
Environmental Policy Impact Assessment: Simulates the environmental effects of proposed regulations, such as emissions caps or renewable energy incentives, assisting in sustainable policy development.
Risk Mitigation Strategies: Assesses political and economic risks associated with new policies and provides insights on risk mitigation measures, supporting resilient governance.

BQ’s Neuro-Symbolic Multi-Agent Platform Adapted for the Government Agencies

Defense and National Security Uses

Supports complex operational planning by generating, simulating, and refining potential Courses of Action (COAs). The platform’s multi-agent structure enables the rapid evaluation of scenarios involving troop movements, resource management, and risk assessment.
Rapid Scenario Evaluation: The platform’s multi-agent structure enables the rapid evaluation of scenarios involving troop movements, resource management, and risk assessment.
Intelligence Integration: Combines intelligence from various sources to provide commanders with a comprehensive operational picture, including real-time threat analysis and situational awareness.
Supply Chain and Logistics Optimization: Models logistical requirements and potential disruptions, enabling efficient allocation of resources in challenging environments.
Cyber Defense Simulation: Assesses cyber vulnerabilities and potential impacts of cyberattacks, providing actionable insights for proactive cyber defense planning.
Multi-Domain Strategy Development: Facilitates cross-domain operations planning, integrating data across air, land, sea, space, and cyber domains for coordinated strategy formation.

Government Awards and Recognitions

Awarded Projects:

BrightQuery has three projects funded by the National Science Foundation (NSF) that involve the development of data access and analysis platforms. While these projects share some broad technical elements with the current proposal, they serve different purposes and target different sectors, ensuring there is no overlap in effort or focus.

1. Data Access Alternatives, Artificial Intelligence-Supported Interfaces

Lead Agency: National Center for Science and Engineering Statistics (NCSES)
Funding Agency: National Science Foundation (NSF)
Award Amount: $1.4M awarded in August 2024
Contract: Project Details
Summary: This project focuses on creating AI-driven interfaces to improve data accessibility for scientific and educational datasets. Although AI and data processing capabilities are part of both efforts, this project is tailored to the needs of academic research and public policy sectors, and its scope does not intersect with the operational or defense-specific requirements of the current Space Force proposal.

2. Building Capacity for State, Local, and Territorial Governments to Use Administrative Data for Evidence-Building

Lead Agency: Bureau of Labor Statistics (BLS)
Funding Agency: National Science Foundation (NSF)
Contract: Project Details
Award Amount: $1.26M awarded in August 2024
Summary: This project supports state and local governments by providing AI tools to utilize administrative data for decision-making. The scope is primarily focused on public policy and government operations, with no overlap in the defense-specific objectives or stakeholders involved in the current Space Force proposal.

3. AI-Ready Data Products to Facilitate Discovery and Use (AI-RD-24)

Lead Agency: Bureau of Economic Analysis (BEA)
Funding Agency: National Science Foundation (NSF)
Contract: Project Details
Award Amount: $1.00M awarded in September 2024
Summary: The objective of this project is to explore how a future National Secure Data Service (NSDS) could provide shared information and tools for making statistical data products more readily ingestible by AI technologies. These resources could support federal agencies and their partners throughout the data and evidence ecosystem as they balance the risks and rewards of leveraging generative AI to expand the reach of their statistics.