Applies A2C and PPO agents with post-hoc explanations to optimal battery control in PV-equipped buildings using real LLEC data and shows cost reduction plus policy insights.
Reviewing the need for explainable artificial intelligence (xai)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.
citing papers explorer
-
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
Applies A2C and PPO agents with post-hoc explanations to optimal battery control in PV-equipped buildings using real LLEC data and shows cost reduction plus policy insights.
-
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.