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...
This work builds upon existing task and motion planning (TAMP) frameworks by integrating pre-trained Sequencing Task-Agnostic Policies (STAP) and Effo...
This paper presents a compelling advancement in Task and Motion Planning (TAMP) by introducing a hierarchical framework designed to tackle long-horizo...
In heuristic search, it is well-established that different types of heuristics are suited for optimal heuristic search (OHS) and unbounded suboptimal...
This paper addresses a crucial gap in the field of heuristic search, specifically concerning bounded-suboptimal search (BSS). The authors rightly high...
The Multi-Agent Warehouse Rearrangement (MAWR) problem calls for computing agents plans such that they collectively rearrange a warehouse environment...
This paper tackles the Multi-Agent Warehouse Rearrangement (MAWR) problem, a complex and practical variant of Multi-Agent Path Finding (MAPF) and Mult...
Maximum Satisfiability (MaxSAT) is an essential framework for combinatorial optimization at the core of automated reasoning. However, to date, no nota...
The paper "From Scalable SAT to MaxSAT: Massively Parallel Solution Improving Search" addresses a critical gap in automated reasoning and combinatoria...
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