A Prolog skill enables LLMs to structure placement intent into facts and queries while delegating constraint reasoning to symbolic logic for cloud-edge service placement.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.DC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A GNN-based DRL model with two actor-critics produces comparable Pareto fronts for multi-objective fog application placement in milliseconds versus hours for genetic algorithms.
citing papers explorer
-
A Neurosymbolic Prolog Skill for LLM-Driven Service Placement
A Prolog skill enables LLMs to structure placement intent into facts and queries while delegating constraint reasoning to symbolic logic for cloud-edge service placement.
-
Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
A GNN-based DRL model with two actor-critics produces comparable Pareto fronts for multi-objective fog application placement in milliseconds versus hours for genetic algorithms.