Tag
This paper proposes a framework that learns admissible cost partitions for planning heuristics by leveraging Lagrangian dual equivalence, using a deep architecture with axial self-attention to guarantee admissibility by construction. It claims to be the first machine-learned heuristic provably guaranteed to be admissible.
This paper proposes Latent Heuristic Search (LHS), a framework that shifts heuristic discovery to a learned continuous latent manifold, using gradient-based optimization and normalizing flows to generate novel heuristics conditioned on large language models, achieving competitive results on TSP, CVRP, KSP, and Online Bin Packing.
This paper proposes property-guided LLM program synthesis, using counterexample-guided inductive synthesis (CEGIS) to provide concrete feedback when a candidate program fails a formal property, reducing the number of generations and evaluation costs. Applied to PDDL planning domains for synthesizing direct heuristic functions, the method outperforms prior approaches, generating seven times fewer programs and solving more tasks without search.
This paper models the Resource-Constrained Project Scheduling Problem as optimal search over a Petri net reachability graph and solves it with A* guided by a consistent heuristic combining critical path and resource lower bounds, outperforming MIP baselines on PSPLIB benchmarks.
This paper introduces HMACE, a heterogeneous multi-agent collaborative evolution framework that uses Large Language Models to automate heuristic design for NP-hard combinatorial optimization problems. It demonstrates improved quality-efficiency trade-offs over single-agent and multi-agent baselines on problems like TSP and BPP.