HAL
Harness for Adaptive Learning
Achieve High-Volume Analytics with HAL, an Artificial Intelligence (AI)-driven, Modeling, Simulation and Analysis (MS&A) Autopilot.
Harness for Adaptive Learning (HAL) is an advanced experimental design, adaptive sampling and machine learning framework that facilitates the rapid understanding of simulation models, algorithms and black-box systems with complex behavior. HAL is designed with a model agnostic architecture that automates multi-processing and optimization, accelerating analytical processes for any modeling environment.
Key Capabilities
State-of-the-Art Sampling
HAL accelerates deep exploration of high-dimensional models using automated space-filling Design of Experiments (DOE) and AI-driven adaptive sampling. With HAL, data generation is fire and forget.
MS&A on Rails
Automates multi-processing and optimization, autogenerates Verification and Validation (V&V) and pairwise comparison reports, utilizes pre-built pipelines for common studies and handles mixed inputs with non-continuous variable types.
Data Exploration
Tracks input/output files and handles model errors and logs. HAL provides an interactive report generator and a fully featured data dashboard for rapid visualization of results.
Trusted Experience
Built for the DoD and Intelligence Community (IC)
HAL is designed to maximize efficiency, commonality, interoperability, accessibility, sustainment and effectiveness of MS&A.
Accelerated MS&A
Automates parallel processing, high-performance computing and adaptive sampling, generating the best synthetic data for analysis and meta-model production.
Rapid Model Development
Slashes model production timelines, increases rigor in red threat modeling and improves compatibility between modeling domains.
Keeping Pace with a Continuous Analysis Process
Thrives in Continuous Integration/Continuous Delivery (CI/CD) pipelines within platforms such as Gitlab, enabling automated model regression testing and analytics.
Confidence in Models
HAL enables a full understanding of models via auto-generated V&V support reports, comparison reports (baseline vs. revised), k-fold cross-validation, feature importance, etc.
Improved Workforce Skills
Turbocharges analysts by simplifying file organization, automation, non-continuous variables, dimensionality, multi-processing, optimization, surrogate modeling and V&V support.
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M9478-2025-KBR-09-2025