Arles Rodríguez

Foraging-Inspired Self-Organisation for Terrain Exploration with Failure-Prone Agents

Mobile ad-hoc sensor systems are employed increasingly for distributed tasks in unreliable conditions, such as terrain exploration and measuring. Here, self-organising solutions can help ensure reliability, availability and scalability, while making use of unreliable components (or agents) with limited resources. These enable agents to act independently, and to exchange and combine their partial solutions into a (more) complete result, which can be transmitted to users before all agents fail. In previous work, we have studied how foraging-inspired self-organisation can help mobile agents achieve a collaborative task -- terrain exploration and information gathering. Obtained results revealed two key aspects impacting success rates: the strategy for exploring as much uncharted terrain as fast as possible, and the strategy for encountering the highest number of agents that hold complementary information. In this paper, we explore further techniques for these aspects and introduce passive pheromone evaporation. Our results show that a hybrid approach improving exploration efficiency features higher success rates than basic stigmergy models, random exploration and Lévy walks.

A didactic e-learning platform with open content navigation and adaptive exercises

In this paper an intelligent platform of virtual learning is presented. The platform supports autonomous learning of students and allows administrators to develop virtual academic programs. Platform has tools for classes and quizzes and generates a map of contents based on the relationship between content units allowing students to navigate the topic in an autonomous way. Platform can represent the relationship between courses in a career, in this case Systems Engineering at Universidad Nacional de Colombia. A course of basic programming and a tool developed to learn functional programming called fρ-L earning are also presented.

Exploring Complex Networks with Failure-Prone Agents

Distributed data-collection and synchronization is essential in sensor networks and the Internet of Things (IoT), as well as for data-replication in server farms, clusters and clouds. Generally, such systems consist of a set of interconnected components, which cooperate and coordinate to achieve a collective task, while acting locally and being failure-prone. An important challenge is hence to define efficient and robust algorithms for data collection and synchronisation in large-scale, distributed and failure-prone platforms. This paper studies the performance and robustness of different multi-agent algorithms in complex networks with different topologies (Lattice, Small-world, Community and Scale-free) and different agent failure rates. Agents proceed from random locations and explore the network to collect local data hosted in each node. Their exploration algorithm determines how fast they cover unexplored nodes to collect new data, and how often they meet other agents to exchange complementary data and speed-up the process. Two exploration algorithms are studied: one random and one using a stigmergy model (that we propose). Experimental results show how network topologies and agent failure-rates impact data-collection and synchronization, and how a stigmergy-based approach can improve performance and success rates across most scenarios. We believe these results offer key insights into the suitability of various decentralised algorithms in different networked environments, which are increasingly at the core of modern information and communication technology (ICT) systems.

Towards a Self-healing Multi-agent Platform for Distributed Data Management

We demonstrate a self-healing multi-agent simulation platform for distributed data-management tasks, including data collection and synchronisation. Collective tasks can be simulated within two types of environments: uncharted terrains with various obstacles, and computing networks with different complex topologies. Agents explore their environment, collect and update local data, and exchange data with agents that they encounter, until the collective task is completed. We have previously implemented several agent exploration algorithms and evaluated their performance in terms of completion speed (essential when agents may fail) and resource overheads (essential in constrained environments). Here, we focus on the agents’ ability to self-heal, via local replication, so as to ensure task completion. We focus on computing network environment, where software replication is more feasible. Envisaged applications include data management in computing clouds, distributed databases, sensor networks, robot swarms and the Internet of Things.

Replication-Based Self-healing of Mobile Agents Exploring Complex Networks

Multi-agent based approaches can offer highly scalable, robust and flexible ways to provide data-collection and synchronisation services in large-scale dynamic distributed environments, ranging from physical terrains to sensor networks, computing Clouds, and the Internet of Things (IoT). The network topology of the targeted distributed system, as well as the agents’ exploration algorithm, have an important impact on service performance and consequently on robustness in case of failure-prone agents. In previous works we have proposed a pheromone-based agent exploration algorithm that performs best in most targeted environments. We have also identified the network topology characteristics that are most sensitive to agent failure. In this paper, we propose a replication-based self-healing approach that enables agents to complete a data-synchronisation task even for high-failure rates, in failure-sensitive network topologies. System nodes can learn and estimate time-outs dynamically, so as to minimise false positives. We evaluate overheads incurred by agent replication, in terms of memory consumption and message communication. The reported findings can help design viable multi-agent solutions for a wide variety of data-intensive distributed systems.