Articles | Sciaria - Science Aria

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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Takumi Shimoda, Alex Fukunaga

Decoupling Generation and Evaluation for Parallel Greedy Best-First Search

Informatics

In order to understand and control the search behavior of parallel search, recent work has proposed a class of constrained parallel greedy best-first...

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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...

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Runzhe Liang, Rishi Veerapaneni, Daniel Harabor, Jiaoyang Li, Maxim Likhachev

Real-Time LaCAM for Real-Time MAPF

Robotics

The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-...

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This paper addresses a significant challenge in Multi-Agent Path Finding (MAPF): the practical limitations of full-horizon planning in real-world, tim...

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Daniel Koch, Stefan Funke

Guiding the Search for the Euclidean Shortest Path Problem

Informatics

We consider the problem of reducing the search space of algorithms which solve the Euclidean Shortest Path Problem by traversing a precomputed navigat...

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This work introduces a novel approach to optimize algorithms for the Euclidean Shortest Path Problem, specifically targeting those that navigate preco...

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Matej Husár, Jiří Švancara, Roman Barták

On Path Selection for Reduction-Based Solving of Multi-Agent Pathfinding Using Graph Pruning

Informatics

Multi-agent pathfinding is the task of navigating a set of mobile agents in a shared environment such that they avoid collisions. Finding an optimal s...

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This paper addresses a fundamental challenge in Multi-Agent Pathfinding (MAPF): the limitations of reduction-based algorithms when applied to larger p...

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Hasan Ferit Eniser, Songtuan Lin, Nicola Müller, Anastasia Isychev, Valentin Wüstholz, Isabel Valera, Jörg Hoffmann, Maria Christakis

Using Action-Policy Testing in RL to Reduce the Number of Bugs

Informatics

Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior wor...

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This paper presents a novel approach to improving the robustness of Reinforcement Learning policies by integrating action-policy testing directly into...

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Shizhe Zhao, Yancheng Wu, Zhongqiang Ren

Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties

Informatics

This paper investigates a Traveling Salesman Problem with Time Windows and Vacant Penalties (TSP-TW-VP), which plans a path to service a set of machin...

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The paper "Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties" investigates a significant extension to the...

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Devin Wild Thomas, Wheeler Ruml

Real-time Cost-algebraic Heuristic Search

Informatics

Planning under time pressure arises in many situations. Real-time heuristic search, in which an agent must compute its next action within a prespecifi...

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The paper "Real-time Cost-algebraic Heuristic Search" addresses a critical challenge in the field of real-time planning: the difficulty in proving the...

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Matan Sudry, Tom Jurgenson, Erez Karpas

Task and Motion Planning Using Infinite Completion Tree and Agnostic Skills

Robotics

This work builds upon existing task and motion planning (TAMP) frameworks by integrating pre-trained Sequencing Task-Agnostic Policies (STAP) and Effo...

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This paper presents a compelling advancement in Task and Motion Planning (TAMP) by introducing a hierarchical framework designed to tackle long-horizo...

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This paper addresses a crucial gap in the field of heuristic search, specifically concerning bounded-suboptimal search (BSS). The authors rightly high...