3 accepted papers
Narrow passage scenarios pose significant challenges for path planning, especially for tasks requiring real-time performance. Traditional asymptotically converging sampling-based planners (SBPs) often exhibit poor initial path quality and slow convergence, limiting their ability to efficiently const
Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in an intermediate, uninterpretable concept space, overlooking the grounding quality of the final subgraph explanations for end users. This gap yields explanations…
Having reliable specifications is an unavoidable challenge in achieving verifiable correctness, robustness, and interpretability of AI systems. Existing specifications for neural networks are in the paradigm of data as specification. That is, the local neighborhood centering around a reference input…