Permutations can be viewed as pairs of linear orders, or more formally as models over a signature consisting of two binary relation symbols. This approach was adopted by Albert, Bouvel and Féray, who studied the expressibility of first-order logic in this setting. We focus our attention on monadic second-order logic. Our results go in two directions. First, we investigate the expressive power of monadic second-order logic. We exhibit natural properties of permutations that can be expressed in monadic second-order logic but not in first-order logic. Additionally, we show that the property of having a fixed point is inexpressible even in monadic second-order logic. Secondly, we focus on the complexity of monadic second-order model checking. We show that there is an algorithm deciding if a permutation $π$ satisfies a given monadic second-order sentence $\varphi$ in time $f(|\varphi|, \operatorname{tw}(π)) \cdot n$ for some computable function $f$ where $n = |π|$ and $\operatorname{tw}(π)$ is the tree-width of $π$. On the other hand, we prove that the problem remains hard even when we restrict the permutation $π$ to a fixed hereditary class $\mathcal{C}$ with mild assumptions on $\mathcal{C}$.
Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan, Nikolaos Melissinos, Michal Opler
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 as fast as possible, which is evaluated by a measure known as the makespan of the proposed solution. This is the setting studied in the Multiagent Path Finding problem. In this work, we additionally provide the agents with a way to communicate with each other. Due to size constraints, it is reasonable to assume that the range of communication of each agent will be limited. What should be the trajectories of the agents to, additionally, maintain a backbone of communication? In this work, we study the Multiagent Path Finding with Communication Constraint problem under the parameterized complexity framework. Our main contribution is three exact algorithms that are efficient when considering particular structures for the input network. We provide such algorithms for the case when the communication range and the number of agents (the makespan resp.) are provided in the input and the network has a tree topology, or bounded maximum degree (has a tree-like topology, i.e., bounded treewidth resp.). We complement these results by showing that it is highly unlikely to construct efficient algorithms when considering the number of agents as part of the input, even if the makespan is $3$ and the communication range is $1$.