Enhancing operations in contested environments
The DoD’s Joint Logistics Enterprise spans supply chain and logistics operations, ensuring forces are ready to go at a moment’s notice. Because this enterprise must operate in an increasingly contested global security environment, DARPA created the LogX program to enhance the enterprise’s ability for real-time situational awareness, future state prediction, and assessment.
STAPLES (State Estimation via Asynchronous Probabilistic Inference for Logistics Enterprises) is an instrumental part of the LogX program. Charles River developed STAPLES software to offer state estimation and prediction at scale.
“By employing probabilistic programming, we can achieve performant, accurate, and automatic inference across a wide range of models.”
Senior Scientist at Charles River Analytics and Principal Investigator for STAPLES
STAPLES and probabilistic programming
STAPLES is implemented within Scruff™, the latest probabilistic programming framework from Charles River Analytics.
By using Scruff, STAPLES can offer state estimation and prediction that is highly performant, distributed, and dynamic. STAPLES incorporates:
- asynchronous belief propagation
- a tractable-by-construction (TBC) approach
- a temporal querying interface
The ultimate goal of probabilistic programming is performant, accurate, and automatic inference across a wide range of models. However, significant expertise is usually required to understand the tradeoffs and challenges. Unlike other probabilistic programming frameworks, Scruff enables high-performance inference with minimal expertise in inference algorithms.
Scruff is built on the success of Charles River’s Figaro™ probabilistic programming language, which has been applied successfully to solve a diverse range of DoD problems
Pioneers in probabilistic programming
Learn more about Charles River’s pioneering work in the field of probabilistic programming from our scientists’ academic journal and conference publications. Key writings include:
Distribution Statement “A” (Approved for Public Release, Distribution Unlimited).
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. N65236-20-C-8005. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DARPA.