Articles | Sciaria - Science Aria

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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Saeid Amiri, Danial Dervovic, Parisa Zehtabi, Michael Cashmore

Surrogate-Assisted Monte-Carlo Tree Search in Facility Location and Beyond (Extended Abstract)

Operations

Combinatorial problems abound in industry. A persistent issue encountered using search-based solutions is that evaluating particular nodes may be expe...

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This extended abstract presents a compelling approach to tackling computationally expensive combinatorial optimization problems, specifically focusing...

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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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Jonathan Morag, Noy Gabay, Daniel Koyfman, Roni Stern

Should Multi-Agent Path Finding Algorithms Coordinate Target Arrival Times?

Robotics

Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configurati...

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This position paper critically examines a fundamental inefficiency in current approaches to Lifelong Multi-Agent Path Finding (LMAPF), specifically th...

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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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Dawson Tomasz, Richard Valenzano

Augmenting Exploration with Locally Greedy Probes

Informatics

Enhancing Greedy Best First Search (GBFS) with stochastic exploration will often greatly improve search performance. In this work, we show that one wa...

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This paper presents a compelling analysis of how stochastic exploration enhances Greedy Best First Search (GBFS), identifying that exploration often a...

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This paper tackles a pertinent challenge in the domain of parallel search, specifically concerning constrained parallel greedy best-first search algor...