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Found 7 papers, 0 papers with code
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
For practical reasons, SMARLA is designed to be black-box (as it does not require access to the internals or training data of the agent) and leverages state abstraction to reduce the state space and thus facilitate the learning of safety violation prediction models from agent's states.
Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis.
Learning Failure-Inducing Models for Testing Software-Defined Networks
Further, we compare FuzzSDN with two state-of-the-art methods for fuzzing SDNs and two baselines for learning failure-inducing models.
An AI-based Approach for Tracing Content Requirements in Financial Documents
Then, FITI uses a combination of rule-based and data-centric approaches, by leveraging information retrieval (IR) and machine learning (ML) techniques that analyze the words, sentences, and contexts related to an information type, to rank candidate sentences.
AI-enabled Automation for Completeness Checking of Privacy Policies
A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR.
Optimal Priority Assignment for Real-Time Systems: A Coevolution-Based Approach
This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines.
Software Engineering
Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)
In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search.
