Game Theory Models for Multi-Robot Patrolling of Infraestructures

Abstract This work is focused on the problem of performing multi‐robot patrolling for infrastructure security applications in order to protect a known environment at critical facilities. Thus, given a set of robots and a set of points of interest, the patrolling task consists of constantly visiting these points at irregular time intervals for security purposes. Current existing solutions for these types of applications are predictable and inflexible. Moreover, most of the previous centralized and deterministic solutions and only few efforts have been made to integrate dynamic methods. Therefore, the development of new dynamic and decentralized collaborative approaches in order to solve the aforementioned problem by implementing learning models from Game Theory. The model selected in this work that includes belief‐based and reinforcement models as special cases is called Experience‐Weighted Attraction. The problem has been defined using concepts of Graph Theory to represent the environment in order to work with such Game Theory techniques. Finally, the proposed methods have been evaluated experimentally by using a patrolling simulator. The results obtained have been compared with previous availabl

Self-organization of patrolling-ant algorithms

Abstract—We consider here multi-agent patrolling as the task for a group of agents to repeatedly visit all the cells of a discrete environment. Wagner et al. [1] have introduced patrolling ant algorithms, where each agent can only mark and move according to its local perception of the environment. Among various results, it has been experimentally observed that for some algorithms the agents often self-organize in stable cycles which are near optimal in terms of visit frequency. This property is particularly interesting as it guarantees the longterm performance of the patrol. The present paper focuses on the convergence behavior of a typical ant-based algorithm, EVAW [1; 2]. The main contribution of this paper is to theoretically prove that the group of agents self-organizes in cycles under certain hypotheses. These hypotheses rely on some implementation details that allow to control the predictability of the system. In addition to these qualitative results on the convergence behavior, we aim at experimentally evaluating its characteristics. This led us to a second contribution: an algorithm that detects steady states. Finally, we propose an improved behavior that dramatically speeds up the self-organization and allows us to experiment on larger problems (both in terms of size and number of agents). I