Asset Type
Docker container
Clingo Answer Set Solver
State-of-the-art solver for logic programming under the answer set semantics.
Verifiable AI
The Verifiable AI objectives are organized in four open research questions that constitute four dimensions of the grand challenge resulting from the emergent use of AI in safety-critical applications. These four dimensions also represent the natural way to organize the background material on Verifiable AI:
- Dependability with AI: how to design and verify dependable and secure systems that include unverifiable AI components?
- Dependability of AI: how to verify the dependability and security of AI components themselves (i.e., domain-independent inference engines, as well as knowledge bases either machine learned from data or manually encoded by human domain experts)?
- AI for dependability: which AI techniques can be themselves leveraged to automate the design and verification of systems that include unverified AI components?
- Meta-AI dependability: which AI techniques can be themselves leveraged to automate the design and verification of AI components (meta-AI)?
Video file
Asset Type
Docker container
Fraunhofer uncertainty metrics for classification tasks
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
Asset Type
Docker container
Wumpus World Simulator
REST web service simulating the Wumpus World intelligent agent environment popularised by Russell & Norvig's seminal textbook Artificial Intelligence: A Modern Approach.
Asset Type
Docker container
VERIFAI
Verifiable and Explainable RIsk Forecasting Artificial Intelligence Framework
Asset Type
Dataset
Adaptive Test Case Selection - Datasets
Data sets to evaluate test case prioritization and selection, finding test cases likely to fail.