Monday, July 24, 2017

Charles River Analytics Inc., developer of intelligent systems solutions, has announced a contract extension for a system supporting Probabilistic Model-Based Programming Techniques for Prediction, Analysis, and Control, or PROMPT. In the PROMPT system, Charles River is developing tools for system engineers to reason under uncertainty. The one-year extension awarded by the Defense Advanced Research Projects Agency (DARPA) is valued close to $500,000.

“We’re helping engineers create system models to reason about the operating behavior of systems in uncertain environments,” said Dr. Avi Pfeffer, Chief Scientist at Charles River Analytics. “We’re developing probabilistic extensions to the standard SysML systems modeling language, which we compile to our Figaro™ probabilistic programming language, allowing engineers to explore potential outcomes and tradeoffs through plugins to their systems engineering tools. Providing accurate analyses and predictions of system performance under uncertainty enables system engineers to create better designs faster, saving time and money.”

PROMPT is designed to extend model-based systems engineering (MBSE) languages with probabilistic programming capabilities. Model-based programming languages are widely used in software design to add safe and effective performance features to mission critical systems. PROMPT enables engineers to link existing MBSE language elements to model-based probabilistic programming language constructs. This produces a probabilistic program which may be executed and analyzed, allowing engineers to reason about the behaviors of systems operating under uncertainty, such as a minesweeping system that relies on noisy sensors for target identification.

PROMPT will also provide inference algorithms that support prediction of system performance and analysis of designs in the face of uncertainty. Inference services are built on the open-source Figaro™ probabilistic programming language.

Read more about Figaro


This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) and the Army Contracting Command-Aberdeen Proving Grounds (ACC-APG) under Contract No. W911NF-15-C-0002. 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 Defense Advanced Research Projects Agency (DARPA) and the Army Contracting Command-Aberdeen Proving Grounds (ACC-APG).

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