A Survey of Heuristics for Domain-Independent Planning

Liang, Ruishi

doi:10.4304/jsw.7.9.2099-2106

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Therefore, Dijkstra's algorithm was chosen as a reference [24]. The algorithm is often used as a benchmark, because it is optimal and represents the worst-case scenario in domain-independent planners [32].…”

Section: Methodsmentioning

confidence: 99%

A* Heuristic Based on a Hierarchical Space Model Extracted from Game Replays

Dzieńkowski

Markowska–Kaczmar

2016

Annals of Computer Science and Information Systems

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Abstract-The paper presents a new method of building a hierarchical model of the state space. The model is extracted fully automatically from game replays that store executed plan traces. It is used by a novel approach for estimating the distance between states in a state-space graph. The estimate is applied in the A* algorithm as a heuristic function to reduce the search space. The method was validated using the game Smart Blocks. It is a testbed environment for studying methods that benefit from game replay analysis. The proposed heuristic is dedicated to difficult classical planning problems, for which problem-specific or automated heuristics are difficult to obtain.

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Section: Methodsmentioning

confidence: 99%

A* Heuristic Based on a Hierarchical Space Model Extracted from Game Replays

Dzieńkowski

Markowska–Kaczmar

2016

Annals of Computer Science and Information Systems

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“…Heuristic search (Bryce & Kambhampati, 2007;Haslum, Botea, Helmert, Bonet, & Koenig, 2007;Liang, 2012) has become a de facto standard method to fin plans in large deterministic search space. This approach has revolutionized planning, both in terms of scalability and in terms of research methodology.…”

Section: Planning Heuristicsmentioning

confidence: 99%

PDDL4J: a planning domain description library for java

Pellier

Fiorino

2017

Journal of Experimental & Theoretical Artificial Intelligen

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Pddl4j (Planning Domain Description Library for Java) is an open source toolkit for Java cross-platform developers meant (1) to provide state-of-the-art planners based on the Pddl language, and (2) to facilitate research works on new planners. In this article, we present an overview of the Automated Planning concepts and languages. We present some planning systems and their most significant applications. Then, we detail the Pddl4j toolkit with an emphasis on the available informative structures, heuristics and search algorithms.

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Configurable Heuristic Adaptation for Improving Best First Search in AI Planning

Serina

Vallati

2020

2020 IEEE 32nd International Conference on Tools With Artificial Intelligence (ICTAI)

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Automated planning is one of the most prominent AI challenges. In the last few decades, there has been a great deal of activity in designing planning techniques and planning engines, with a focus on forward state-space search. Despite the ubiquitous use of heuristics in AI planning, these techniques are susceptible to being easily trapped by undetected dead ends and huge search plateaus.In this paper we introduce a highly configurable heuristic adaptation process based on the idea of dynamically penalising unpromising actions when an inconsistency in the heuristic evaluation is detected; its aim is to reduce the bias affecting specific actions, thereby encouraging exploration by the search process and adding diversity in the neighbourhood selection process. Our extensive experimental analysis demonstrates that the proposed heuristic can be configured to improve significantly the performance of best first search planning on a range of benchmark domains.

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A Survey of Heuristics for Domain-Independent Planning

Cited by 4 publications

References 25 publications

A* Heuristic Based on a Hierarchical Space Model Extracted from Game Replays

A* Heuristic Based on a Hierarchical Space Model Extracted from Game Replays

PDDL4J: a planning domain description library for java

Configurable Heuristic Adaptation for Improving Best First Search in AI Planning

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