Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to specify, validate, and r...
Eduardo de Conto, B. Genest, A. Easwaran et al.· 0 citations
This work proposes a hierarchical partitioning-and-mapping framework, HyperCut, that enables early filtering of inter-layer schedules based on hypergraph partitioning that achieves 2.0x performance improvement and 80.47% exploration time reduction over the SET baseline, measured by geometric mean.
Ziang Wei, Zirui Xu, Sufeng Guo et al.· 0 citations
Evaluation of this approach using a state-of-the-art commercial solver with stochastic EV rental requests under different confidence levels and time-varying electricity prices demonstrates significant benefits of the integrated mechanism design, including a reduction in charging costs and battery capacity degradation c...
This work proposes neurosymbolic HRL with {\em Incremental Knowledge (InK), where symbolic high-level components perform symbolic planning on an updatable representation of current InK, while low-level goal-conditioned neural modules learn motion primitives through experience using reward shaping.
Subrat Prasad Panda, B. Genest, A. Easwaran· Lecture notes in computer sc...· 0 citations
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