A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search

Fickert, Maximilian

doi:10.1609/icaps.v30i1.6652

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…As future work, we aim to investigate other techniques for improving LAO* for FOND planning, so we intend to look into other updating functions, use and adapt modern FOND planning heuristics [38], design explicitly tie-breaking strategies for LAO*, and employ other satisficing planning techniques in LAO*, such as preferred operators detection and selection [43], deferred evaluation [43], Pareto [45], look-ahead strategies for delete-relaxation heuristics [19], and etc.…”

Section: Discussionmentioning

confidence: 99%

Scaling-Up LAO* in FOND Planning: An Ablation Study

Fraga Pereira

2023

Frontiers in Artificial Intelligence and Applications

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The use of multi-queue heuristic search and tie-breaking strategies has shown to be very effective for satisficing planning in the Classical Planning setting. However, to the best of our knowledge, the use of such techniques has never been studied and employed in heuristic search algorithms for Fully Observable Non-Deterministic (FOND) Planning. In this paper, we adapt existing satisficing techniques for scaling-up an AND/OR heuristic search algorithm for FOND Planning. Namely, we employ multi-queue heuristic search, dead-end detection, and tie-breaking strategies in LAO* for improving the extraction of strong-cyclic policies. We assess the efficiency of our techniques in LAO* through an extensive ablation study over two different FOND Planning benchmarks. Empirical results show that our techniques improve the performance of LAO* in terms of coverage, expanded nodes, and planning time compared to a well-known planner based on vanilla LAO*. Indeed, the best configuration of our techniques is competitive with the current state-of-the-art in FOND Planning.

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Section: Discussionmentioning

confidence: 99%

Scaling-Up LAO* in FOND Planning: An Ablation Study

Fraga Pereira

2023

Frontiers in Artificial Intelligence and Applications

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“…Fickert and Hoffmann (2017) proposed a different approach, which escapes local minima by refining a delete relaxation heuristic and changing heuristic values of nodes. Recent work has proposed a lookahead strategy for GBFS, which tries to find a node with a lower h-value than the expanded node at each expansion using both of novelty and delete relaxation heuristics (Fickert 2020). While the notions of novelty and delete relaxation heuristics used in these methods are limited to particular graph search problems such as classical planning, our approach does not have such a limitation.…”

Section: Related Workmentioning

confidence: 99%

Biased Exploration for Satisficing Heuristic Search

Kuroiwa¹,

Beck²

2022

ICAPS

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Satisficing heuristic search such as greedy best-first search (GBFS) suffers from local minima, regions where heuristic values are inaccurate and a good node has a worse heuristic value than other nodes. Search algorithms that incorporate exploration mechanisms in GBFS empirically reduce the search effort to solve difficult problems. Although some of these methods entirely ignore the guidance of a heuristic during their exploration phase, intuitively, a good heuristic should have some bound on its inaccuracy, and exploration mechanisms should exploit this bound. In this paper, we theoretically analyze what a good node is for satisficing heuristic search algorithms and show that the heuristic value of a good node has an upper bound if a heuristic satisfies a certain property. Then, we propose biased exploration mechanisms which select lower heuristic values with higher probabilities. In the experiments using synthetic graph search problems and classical planning benchmarks, we show that the biased exploration mechanisms can be useful. In particular, one of our methods, Softmin-Type(h), significantly outperforms other GBFS variants in classical planning and improves the performance of Type-LAMA, a state-of-the-art classical planner.

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“…A best-first é uma técnica heurística, que utiliza o conceito de fila prioritária, seguindo uma regra específica [27]. Neste trabalho a técnica foi implementada para alocar os recursos dos dispositivos na EC que tem menor disponibilidade de um recurso.…”

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