PRINCESS

Overview

PRINCESS (Probabilistic Representation of Intent Commitments to Ensure Software Survival) is a project under the DARPA BRASS (Building Resource Adaptive Software Systems) program. It is led by Charles River Analytics and involves partners from the university of Birmingham, Harvard University and the University of Southern California.

The overall aim of the project is to develop adaptive software systems for operation in dynamic and uncertain environments, underpinned by rigorous formal verification methods which ensure the correctness of the adaptation process. The work will involve developing techniques that combine verification with probabilistic modelling and inference, with a particular preference for the use of probabilistic programming languages.

Current participants (at Birmingham):

Publications

8 publications:
  • [PFF+19] Avi Pfeffer, Curt Wu, Gerald Fry, Kenny Lu, Steve Marotta, Mike Reposa, Yuan Shi, T. K. Satish Kumar, Craig A. Knoblock, David Parker, Irfan Muhammad and Chris Novakovic. Software Adaptation for an Unmanned Undersea Vehicle. IEEE Software, 36(2), pages 91-96, IEEE. March 2019. [pdf] [bib] [Summarises the PRINCESS project, which develops methods for adapting an optimising software at runtime, including mission verification using extensions of PRISM.]
  • [EPB18] Alexandros Evangelidis, David Parker and Rami Bahsoon. Performance Modelling and Verification of Cloud-based Auto-Scaling Policies. Future Generation Computer Systems, 87, pages 629-638, Elsevier. October 2018. [pdf] [bib] [Presents an approach for producing formal performance guarantees for cloud-based auto-scaling policies using PRISM.]
  • [FLHP18] Fatma Faruq, Bruno Lacerda, Nick Hawes and David Parker. Simultaneous Task Allocation and Planning Under Uncertainty. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'18), IEEE. October 2018. [pdf] [bib] [Proposes techniques for verified multi-robot task allocation and planning, implemented as an extension of PRISM.]
  • [KNP17b] Marta Kwiatkowska, Gethin Norman and David Parker. Symbolic Verification and Strategy Synthesis for Linearly-Priced Probabilistic Timed Automata. In Models, Algorithms, Logics and Tools, volume 10460 of LNCS, pages 289-309, Springer. August 2017. [pdf] [bib] [Proposes novel symbolic techniques for verifi cation and optimal strategy synthesis for priced probabilistic timed automata ]
  • [BKLPW17] Christel Baier, Joachim Klein, Linda Leuschner, David Parker and Sascha Wunderlich. Ensuring the Reliability of Your Model Checker: Interval Iteration for Markov Decision Processes. In Proc. 28th International Conference on Computer Aided Verification (CAV'17), volume 10426 of LNCS, pages 160-180, Springer. July 2017. [pdf] [bib] [Proposes "interval iteration" techniques for deriving more accurate bounds on the results of probabilistic model checking, and implements them in PRISM.]
  • [KNP17] Marta Kwiatkowska, Gethin Norman and David Parker. Probabilistic Model Checking: Advances and Applications. In R. Drechsler (editor), Formal System Verification, pages 73-121, Springer. June 2017. [pdf] [bib] [Provides an introduction to probabilistic model checking, including coverage of some recent advances in the field and a wide variety of examples and applications.]
  • [LPH17] Bruno Lacerda, David Parker and Nick Hawes. Multi-objective Policy Generation for Mobile Robots under Probabilistic Time-Bounded Guarantees. In Proc. 27th International Conference on Automated Planning and Scheduling (ICAPS'17), pages 504-512, AAAI Press. June 2017. [pdf] [bib] [Proposes techniques for generating mobile robot controllers with probabilistic time-bounded guarantees using multi-objective probabilistic model checking and an extension of PRISM.]
  • [NPZ17] Gethin Norman, David Parker and Xueyi Zou. Verification and Control of Partially Observable Probabilistic Systems. Real-Time Systems, 53(3), pages 354-402, Springer. April 2017. [pdf] [bib] [Presents techniques and a tool for verification and strategy synthesis in partially observable probabilistic models with both discrete and dense models of time.]