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...
This survey paper addresses a highly relevant and impactful topic: the application of multi-armed bandit (MAB) algorithms to the Boolean Satisfiabilit...
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...
This paper presents a timely and critical reevaluation of Large Neighborhood Search (LNS) approaches for Multi-Agent Path Finding (MAPF). The authors...
Enhancing Greedy Best First Search (GBFS) with stochastic exploration will often greatly improve search performance. In this work, we show that one wa...
This paper presents a compelling analysis of how stochastic exploration enhances Greedy Best First Search (GBFS), identifying that exploration often a...
In order to understand and control the search behavior of parallel search, recent work has proposed a class of constrained parallel greedy best-first...
This paper tackles a pertinent challenge in the domain of parallel search, specifically concerning constrained parallel greedy best-first search algor...
The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-...
This paper addresses a significant challenge in Multi-Agent Path Finding (MAPF): the practical limitations of full-horizon planning in real-world, tim...
We consider the problem of reducing the search space of algorithms which solve the Euclidean Shortest Path Problem by traversing a precomputed navigat...
This work introduces a novel approach to optimize algorithms for the Euclidean Shortest Path Problem, specifically targeting those that navigate preco...
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...
This paper addresses a fundamental challenge in Multi-Agent Pathfinding (MAPF): the limitations of reduction-based algorithms when applied to larger p...
Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior wor...
This paper presents a novel approach to improving the robustness of Reinforcement Learning policies by integrating action-policy testing directly into...
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...
The paper "Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties" investigates a significant extension to the...
Planning under time pressure arises in many situations. Real-time heuristic search, in which an agent must compute its next action within a prespecifi...
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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