Charles River Analytics, a developer of intelligent systems solutions, announces a contract to develop probabilistic model-based programming techniques for prediction, analysis, and control. These techniques will support the development of more robust and effective systems for DoD applications. The two-year contract awarded by the Defense Advanced Research Projects Agency (DARPA) is valued close to $1 million, with an option to extend, if exercised, for $500,000.
The new solution, called PROMPT, will enhance model-based programming languages by providing support for probabilistic programming. Model-based programming languages are widely used in software design to add safe and effective performance features to mission critical systems. Enhancing these languages with probabilistic capabilities will allow engineers to reason about the behaviors of these systems when operating under uncertainty, such as a minesweeping system that relies on noisy sensors for target identification.
“We are helping engineers create system models they can use to reason about and control the operating behavior of systems in uncertain environments,” explained Dr. Avi Pfeffer, Principal Scientist at Charles River Analytics. “We’re providing probabilistic extensions to the standard SysML systems modeling language and compiling it to our Figaro probabilistic programming language. We’re also incorporating techniques from partially observable Markov decision processes to control the system using the model.”
Figaro is a free, open-source probabilistic programming language for probabilistic modeling. Figaro makes it possible to express probabilistic models using the power of programming languages, giving the modeler the expressive tools to create various types of models. Read more about Figaro.
To learn more about PROMPT or our other current projects and capabilities, contact us.
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).
