Visible to the public Model-Based Explanation For Human-in-the-Loop Security - July 2018Conflict Detection Enabled

PI(s), Co-PI(s), Researchers: David Garlan, Bradley Schmerl (CMU)

Human Behavior
Resilient Architectures


Javier Camara, Wenxin Peng, David Garlan and Bradley Schmerl. Reasoning about Sensing Uncertainty and its Reduction in Decision-Making for Self-Adaptation. In Science of Computer Programming, 2018. Accepted for publication. Volume 167, Dec 1, 2018, Pgs 51-69.

**Updated 10/16/18, Jamie Lou Hagerty

Roykrong Sukkerd, Reid Simmons and David Garlan. Towards Explainable Multi-Objective Probabilistic Planning. In Proceedings of the 4th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS\'18), Gothenburg, Sweden, 27 May2018.
Ryan Wagner, David Garlan and Matthew Fredrikson. Poster: Quantitative Underpinnings of Secure, Graceful Degradation. In Proceedings of the 2018 Symposium of Hot Topics in the Science of Security, Raleigh, North Carolina, 10-11 April 2018.


- The reasoning behind automated planning is opaque to end-users. They may not understand why a particular behavior is generated, and therefore not be able to calibrate their confidence in the systems working properly. To address this problem, we developed a method to automatically generate verbal explanation of multi-objective probabilistic planning, that explains why a particular behavior is generated on the basis of the optimization objectives.

- Adaptive systems are expected to adapt to unanticipated run-time events using imperfect information about themselves, their environment, and goals. This entails handling the effects of uncertainties in decision-making, which are not always considered as a first-class concern. We developed a formal analysis technique that explicitly considers uncertainty in sensing when reasoning about the best way to adapt, together with uncertainty reduction mechanisms to improve system utility.



We have an undergraduate student from Berkeley wokring with us as part of ISRs NSF-funded Research Experience for Undergraduates