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20232026
most citedExact Algorithms and Lowerbounds for Multiagent Pathfinding: Power of Treelike Topology

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.DS2026

Parameterized Complexity of Scheduling Problems in Robotic Process Automation

Michal Dvořák, Antonín Novák, Přemysl Šůcha +2

This paper studies the growing domain of Robotic Process Automation (RPA) problems. Motivated by scheduling problems arising in RPA, we study the parameterized complexity of the si…

cs.DS2025

Pathfinding in Self-Deleting Graphs

Michal Dvořák, Dušan Knop, Michal Opler +3

In this paper, we study the problem of pathfinding on traversal-dependent graphs, i.e., graphs whose edges change depending on the previously visited vertices. In particular, we st…

cs.DS2025

Parameterized Critical Node Cut Revisited

Dušan Knop, Nikolaos Melissinos, Manolis Vasilakis

We study how to sparsify connectivity in graphs under a tight deletion budget. Given a graph and integers , Critical Node Cut (CNC) asks whether we can delete at mos…

cs.CC2024

Solving Multiagent Path Finding on Highly Centralized Networks

Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan +3

The Mutliagent Path Finding (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destin…

cs.CC2024

Exact Algorithms for Multiagent Path Finding with Communication Constraints on Tree-Like Structures

Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan +2

Consider the scenario where multiple agents have to move in an optimal way through a network, each one towards their ending position while avoiding collisions. By optimal, we mean…

cs.CC2023★ 1 cited

Exact Algorithms and Lowerbounds for Multiagent Pathfinding: Power of Treelike Topology

Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan +2

In the Multiagent Path Finding problem (MAPF for short), we focus on efficiently finding non-colliding paths for a set of agents on a given graph , where each agent seeks a…