Category: Informatics
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Grigorios Mouratidis, Bernhard Nebel, Sven Koenig
You May Split but You Might Work It Out Later: First Steps Toward Merging Nodes in MAPF (Extended Abstract)
Informatics

CBS is a state-of-the-art MAPF algorithm whose performance has been enhanced over the years by the introduction of heuristics that focus the search an...

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This extended abstract presents an intriguing and potentially impactful novel approach to enhance the efficiency of Conflict-Based Search (CBS) for Mu...

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Mohammadreza Hami, Nathan Sturtevant
Suboptimal Search with Dynamic Distribution of Suboptimality (Extended Abstract)
Informatics

In bounded-suboptimal heuristic search, the aim is to find a solution path within a given bound as quickly as possible, which is crucial when computat...

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The proposed algorithm, Dynamic Suboptimality Weighted A* (DSWA*), addresses a critical challenge in bounded-suboptimal heuristic search: the static n...

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Lukáš Chrpa, Mauro Vallati
Critical Section Macros - New Results (Extended Abstract)
Informatics

This extended abstract presents new empirical results of recently introduced Critical Section Macro-operators (CSMs) whose design is inspired by using...

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This extended abstract presents timely new empirical results concerning Critical Section Macro-operators (CSMs), a concept inspired by the use of lock...

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Sandra Castellanos-Paez, Francesco Percassi, Mauro Vallati
Exploring the Trade-off Between Flexible and Deployable Models for PDDL+ Urban Traffic Control (Extended Abstract)
Informatics

The problem of traffic signal optimisation has been successfully tackled using the PDDL+ planning formalism, which also provides an ideal ground for s...

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This extended abstract presents a timely and relevant exploration into the core dilemma faced when applying advanced AI planning techniques, specifica...

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Valerio Borelli, Alfonso Emilio Gerevini, Enrico Scala, Ivan Serina
Learning Heuristic Functions with Graph Neural Networks for Numeric Planning (Extended Abstract)
Informatics

In this paper, we investigate the application of heuristics based on Graph Neural Networks (GNNs) to lifted numeric planning problems, an area that ha...

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This extended abstract presents a timely investigation into the application of Graph Neural Networks (GNNs) for learning heuristic functions in the co...

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Luigi Bonassi, Francesco Percassi, Enrico Scala
BLAST: Bit-Blasting Numbers for Classical Planning (Extended Abstract)
Informatics

It is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite. This bounded formulation can be p...

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This extended abstract, titled "BLAST: Bit-Blasting Numbers for Classical Planning," tackles a crucial gap between theoretical tractability and practi...

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Shahaf Shperberg, Lior Siag, Nathan Sturtevant, Ariel Felner
Position Paper: On the Impact of Direction-Selection in BAE*
Informatics

BAE*, and the independently developed DIBBS, are state-of-the-art bidirectional heuristic search algorithms that exploit heuristic consistency to effi...

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This position paper tackles a critical efficiency concern within state-of-the-art bidirectional heuristic search algorithms, particularly BAE* and DIB...

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André Schidler, Stefan Szeider
Extracting Problem Structure with LLMs for Optimized SAT Local Search
Informatics

Encoding combinatorial problems in terms of propositional satisfiability (SAT) enables utilization of highly efficient SAT solvers for combinatorial s...

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This position paper introduces a compelling and novel approach to enhance SAT local search through the specialized application of Large Language Model...

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Zhihui Xie, Xu Liu, Shuai Li
Multi-armed Bandit Algorithms for the Boolean Satisfiability Problem: A Survey
Informatics

This paper provides a survey of recent literature on the use of multi-armed bandit algorithms to solve the Boolean satisfiability problem (SAT), a wel...

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This survey paper addresses a highly relevant and impactful topic: the application of multi-armed bandit (MAB) algorithms to the Boolean Satisfiabilit...

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Jiaqi Tan, Yudong Luo, Jiaoyang Li, Hang Ma
Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities
Informatics

Multi-Agent Path Finding (MAPF) aims to arrange collision-free goal-reaching paths for a group of agents. Anytime MAPF solvers based on large neighbor...

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This paper presents a timely and critical reevaluation of Large Neighborhood Search (LNS) approaches for Multi-Agent Path Finding (MAPF). The authors...

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