no code implementations • 20 Feb 2023 • Dhaminda B. Abeywickrama, James Wilson, Suet Lee, Greg Chance, Peter D. Winter, Arianna Manzini, Ibrahim Habli, Shane Windsor, Sabine Hauert, Kerstin Eder
The behaviours of a swarm are not explicitly engineered.
no code implementations • 21 Jan 2023 • Abanoub Ghobrial, Hamid Asgari, Kerstin Eder
We conduct a case study using YOLOv5 on persons detection to demonstrate our method and usage of the trustworthiness score.
no code implementations • 1 Jul 2022 • Xuan Zheng, Kerstin Eder, Tim Blackmore
A supervised ML algorithm, as a prevalent option in the previous work, is used to bias the test generation or filter the generated tests.
no code implementations • 22 Jun 2022 • Dhaminda B. Abeywickrama, Amel Bennaceur, Greg Chance, Yiannis Demiris, Anastasia Kordoni, Mark Levine, Luke Moffat, Luc Moreau, Mohammad Reza Mousavi, Bashar Nuseibeh, Subramanian Ramamoorthy, Jan Oliver Ringert, James Wilson, Shane Windsor, Kerstin Eder
The main contribution of this article is a set of high-level intellectual challenges for the autonomous systems community related to specifying for trustworthiness.
no code implementations • 19 May 2022 • Nyasha Masamba, Kerstin Eder, Tim Blackmore
Efficient and effective testing for simulation-based hardware verification is challenging.
no code implementations • 17 May 2022 • Nyasha Masamba, Kerstin Eder, Tim Blackmore
Constrained random test generation is one of the most widely adopted methods for generating stimuli for simulation-based verification.
1 code implementation • 30 Apr 2022 • Abanoub Ghobrial, Xuan Zheng, Darryl Hond, Hamid Asgari, Kerstin Eder
In this paper, we propose to reduce such threats by investigating how DNN classifiers can adapt their knowledge to learn new information in the AS's operational environment, using only a limited number of observations encountered sequentially during operation.
no code implementations • 29 Apr 2021 • Anas Shrinah, Derek Long, Kerstin Eder
This paper introduces an approach to validate the functional equivalence of planning domain models.
no code implementations • 19 Jun 2020 • Jose Nunez-Yanez, Kris Nikov, Kerstin Eder, Mohammad Hosseinabady
This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs.
Other Computer Science
1 code implementation • 22 Nov 2018 • Anas Shrinah, Kerstin Eder
The verification of planning domain models is crucial to ensure the safety, integrity and correctness of planning-based automated systems.
no code implementations • 16 Sep 2016 • Dejanira Araiza-Illan, Anthony G. Pipe, Kerstin Eder
In this paper, we compare using Belief-Desire-Intention (BDI) agents as models for test generation with more conventional automata-based techniques that exploit model checking, in terms of practicality, performance, transferability to different scenarios, and exploration (`coverage'), through two case studies: a cooperative manufacturing task, and a home care scenario.
no code implementations • 8 Apr 2014 • Kerstin Eder, Chris Harper, Ute Leonards
The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot.
