تقرير
Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software
| العنوان: | Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software |
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| المؤلفون: | Lyons, D. M., Zahra, S. |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Software Engineering |
| الوصف: | It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an a-priori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the data-flow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility is calculated, an empirical and non-invasive characterization of the inherent objectives of the software designers. By comparing off-line (a-priori) utility with on-line (deployed system) utility, we show, using a small but real ROS example, that it's possible to monitor a performance criterion and relate violations of the criterion to parts of the software. The software is then patched using automated software repair techniques and evaluated against the original off-line utility. Comment: IEEE Workshop on Assured IEEE Workshop on Assured Autonomous Systems, May, 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2005.03813 |
| رقم الأكسشن: | edsarx.2005.03813 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |