AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT
Overview
This paper introduces the Architecturally Admissible Compressor (AAC), a method that adapts landmark-based A* heuristics to query demand within a fixed memory budget. The authors provide two continuous relaxations of the ALT algorithm, proving that relaxed and discrete optima coincide under exact landmark distances. AAC-CG, utilizing fractional relaxation and entropy regularization, achieves an approximation guarantee with a limiting factor of 1-1/e. Experiments on six road networks and two synthetic graphs show that AAC-CG reduces A* expansions by 1.9% to 22.7% compared to farthest-point ALT across 19 tested combinations of graph and memory budget, while achieving 99.6% of canonical greedy's mean heuristic value.
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Reported quantity · percent reduction in A expansions: 1.9 other · Basis not reported · Differing source assertions
“e selection. For expected heuristic value, we prove an approximation guarantee with finite-step, oracle-error, and finite-sample terms. The limiting $1-1/e$ factor is optimal for arbitrary supplied pools and query distributions unless P${=}$NP. At matched label memory, AAC-CG reduces A* expansions by $1.9$-$22.7$% relative to farthest-point ALT (FPS-ALT) in all $19$ tested combinations of graph and memory budget acro”
Exact source · revision 1Source owner not reported
Reported quantity · percent reduction in A expansions: 22.7 other · Basis not reported · Differing source assertions
“e selection. For expected heuristic value, we prove an approximation guarantee with finite-step, oracle-error, and finite-sample terms. The limiting $1-1/e$ factor is optimal for arbitrary supplied pools and query distributions unless P${=}$NP. At matched label memory, AAC-CG reduces A* expansions by $1.9$-$22.7$% relative to farthest-point ALT (FPS-ALT) in all $19$ tested combinations of graph and memory budget acro”
Exact source · revision 1Source owner not reported
Reported quantity · percent of canonical greedy mean heuristic value: 99.6 other · Basis not reported
“ss six road networks and two synthetic graphs. Each combination uses one $500$-query test split; all methods share distance tables when distances are symmetric. Canonical greedy also expands fewer nodes than FPS-ALT in all $19$ graph-budget pairs. AAC-CG achieves $99.6$% of canonical greedy's mean heuristic value on test queries, averaged over these pairs. AAC makes admissibility independent of training success, whil”
Exact source · revision 1Source owner not reported
Reported quantity · number of road networks tested: 6 other · Basis not reported
“ss six road networks and two synthetic graphs. Each combination uses one $500$-query test split; all methods share distance tables when distances are symmetric. Canonical greedy also expands fewer nodes than FPS-ALT in all $19$ graph-budget pairs. AAC-CG achieves $99.6$% of canonical greedy's mean heuristic value on test queries, averaged over these pairs. AAC makes admissibility independent of training success, whil”
Exact source · revision 1Source owner not reported
Reported quantity · number of synthetic graphs tested: 2 other · Basis not reported
“ss six road networks and two synthetic graphs. Each combination uses one $500$-query test split; all methods share distance tables when distances are symmetric. Canonical greedy also expands fewer nodes than FPS-ALT in all $19$ graph-budget pairs. AAC-CG achieves $99.6$% of canonical greedy's mean heuristic value on test queries, averaged over these pairs. AAC makes admissibility independent of training success, whil”
Exact source · revision 1Source owner not reported
Reported quantity · number of graph-budget pairs tested: 19 other · Basis not reported · Differing source assertions
“e selection. For expected heuristic value, we prove an approximation guarantee with finite-step, oracle-error, and finite-sample terms. The limiting $1-1/e$ factor is optimal for arbitrary supplied pools and query distributions unless P${=}$NP. At matched label memory, AAC-CG reduces A* expansions by $1.9$-$22.7$% relative to farthest-point ALT (FPS-ALT) in all $19$ tested combinations of graph and memory budget acro”
Exact source · revision 1Source owner not reported
Reported quantity · number of graph-budget pairs tested: 19 other · Basis not reported · Differing source assertions
“ss six road networks and two synthetic graphs. Each combination uses one $500$-query test split; all methods share distance tables when distances are symmetric. Canonical greedy also expands fewer nodes than FPS-ALT in all $19$ graph-budget pairs. AAC-CG achieves $99.6$% of canonical greedy's mean heuristic value on test queries, averaged over these pairs. AAC makes admissibility independent of training success, whil”
Exact source · revision 1Source owner not reported
Reported quantity · number of query test splits per combination: 1 other · Basis not reported
“ss six road networks and two synthetic graphs. Each combination uses one $500$-query test split; all methods share distance tables when distances are symmetric. Canonical greedy also expands fewer nodes than FPS-ALT in all $19$ graph-budget pairs. AAC-CG achieves $99.6$% of canonical greedy's mean heuristic value on test queries, averaged over these pairs. AAC makes admissibility independent of training success, whil”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT
This paper introduces the Architecturally Admissible Compressor (AAC), a method that adapts landmark-based A* heuristics to query demand within a fixed memory budget. The authors provide two continuous relaxations of the ALT algorithm, proving that relaxed and discrete optima coincide under exact landmark distances. AAC-CG, utilizing fractional relaxation and entropy regularization, achieves an approximation guarantee with a limiting factor of 1-1/e. Experiments on six road networks and two synthetic graphs show that AAC-CG reduces A* expansions by 1.9% to 22.7% compared to farthest-point ALT across 19 tested combinations of graph and memory budget, while achieving 99.6% of canonical greedy's mean heuristic value.
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