5 accepted papers
Graph patrolling algorithms provide effective strategies for coordinating mobile robots in the context of autonomously surveilling valuable assets. Optimizing patrolling strategies often aims to minimize the time between subsequent visits to a vertex, a measure known in the literature as idleness. I…
Graph-patrolling problems in the adversarial domain typically embed models and assumptions about how hostile events, from which an environment must be protected, are generated at a specific time and location. Relying upon such attacker models prevents algorithms from synthesizing strategies that can…
We present a novel algorithm to solve the stochastic orienteering problem with chance constraints that combines Monte Carlo Tree Search (MCTS) with a best arm identification (BAI) algorithm. This method extends our recently proposed solution that builds a search planning tree considering both an obj…
We study a patrolling problem where multiple agents are tasked with protecting an environment where one or more adversaries are trying to compromise targets of varying value. The objective of the patrollers is to move between targets to quickly spot when an attack is taking place and then diffuse it…
The use of autonomous robots for surveillance is one of the most interesting applications of graph-patrolling algorithms. In recent years, considerable effort has been devoted to tackling the problem of efficiently computing effective patrolling strategies. One of the mainstream approaches is advers…