Maintaining Ad-Hoc Communication Network in Area Protection Scenarios with Adversarial Agents

TL;DR

This paper investigates strategies for defending an area in a graph-based environment with mobile agents, focusing on maintaining connectivity among defenders while blocking attackers, and evaluates their effectiveness across various scenarios.

Contribution

It introduces strategies for allocating defending agents to maintain connectivity and block attackers, with an analysis of their performance across different instance types.

Findings

Connectivity-maintaining strategies vary in success depending on scenario.

Supporting non-blocking defenders enhances overall defense effectiveness.

Different strategies are suitable for different types of instances.

Abstract

We address a problem of area protection in graph-based scenarios with multiple mobile agents where connectivity is maintained among agents to ensure they can communicate. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can occupy a vertex; and they can move into adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim of one team - attackers - is to invade into given area while the aim of the opponent team - defenders - is to protect the area from being entered by attackers by occupying selected vertices. The team of defenders need to maintain connectivity of vertices occupied by its own agents in a visibility graph. The visibility graph models possibility of communication between pairs of vertices. We study strategies for allocating vertices to be occupied by the team of defenders to block attacking agents where connectivity is maintained at the same time. To do this we reserve a subset of defending agents that do not try to block the attackers but instead are placed to support connectivity of the team. The performance of strategies is tested in multiple benchmarks. The success of a strategy is heavily dependent on the type of the instance, and so one of the contributions of this work is that we identify suitable strategies for diverse instance types.