Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

GPGPU Computing for Microscopic Simulations of Crowd Dynamics

We compare GPGPU implementations of two popular models of crowd dynamics. Specifically, we consider a continuous social force model, based on differential equations (molecular dynamics) and a discrete social distances model based on non-homogeneous cellular automata. For comparative purposes both models have been implemented in two versions: on the one hand using GPGPU technology, on the other hand using CPU only. We compare some significant characteristics of each model, for example: performance, memory consumption and issues of visualization. We also propose and test some possibilities for tuning the proposed algorithms for efficient GPU computations

Quickest Paths in Simulations of Pedestrians

This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte

A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

HPC applications for data-driven agent-based models of pedestrian movement

La gestió de grans instal·lacions multiús és un procés complicat, que té a veure en trobar un equilibri entre la satisfacció del client, aspectes de seguretat i els interessos comercials. Aquest repte s'accentua en períodes de transició, com èpoques de construcció o la millora i manteniment de la instal·lació. Tot i això, amb el creixement de l'Internet of Things(IoT) i l'accés a HPC per a usos comercials als darrers anys ha proporcionat una manera d'adreçar aquest repte a través d'aquestes tecnologies. Les dades provinents d'aquests sistemes fortament monitorats, combinats amb IA i tècniques de simulació, permeten una nova manera d'abordar la gestió de grans instal·lacions. En aquest Treball de Fi de Grau s'ha desenvolupat un Digital Twin del recinte insígnia del Futbol Club Barcelona: el Camp Nou. L'aspecte més important en el funcionament del recinte són els fluxos de vianants i la seva optimització, assegurar un pla robust enfront de les emergències i gestionar els canvis relacionats amb el projecte de construcció que consisteix en la renovació del recinte del Camp Nou també són prioritaris. Aquest prototip de Model de Moviment de Vianants mostra la viabilitat de combinar diverses fonts de dades amb l'objectiu de representar diversos escenaris relacionats amb la gestió de multituds. Aquest model servirà com a referència per integrar les dades provinents de sensors i preprocessades utilitzant tècniques de Machine Learning. Aquest model estarà integrat amb l'estructura de tot IoTwins dins els test-beds 5 i 11 que se centren en les instal·lacions del FCB. El mètode escollit per la simulació del projecte és la Simulació Basada en Agents. Un paradigma de simulació cada vegada més popular que és capaç de representar poblacions heterogènies que estan formades per agents individuals que representen els vianants, per exemple. El seu moviment està definit per un algoritme desenvolupat específicament que representa l'espai al voltant dels Agents de manera matemàtica, derivada d'una funció de cost que combina diferents factors que afecten els vianants. Els Agents entren al recinte, es mouen seguint les seves prioritats i després en surten seguint el seu camí individual. El model es validarà i calibrarà amb les dades accessibles en aquest moment. Diversos escenaris d'exemple han demostrat la viabilitat del model per optimitzar l'evacuació en cas d'emergència i les afectacions que comporten les renovacions del recinte.The management of large multinational facilities is a complex process involving finding the balance between customer satisfaction, safety concerns and commercial interests. This challenge is particularly pronounced in periods of transitions, such as stages construction work, facility upgrade and maintenance. However, with the growth of the Internet of Things (IoT) and unlocking of HPC for commercial endeavours in recent years offers to address this challenge through the use of technology. The increasing amount of data coming from these heavily monitored system, combined with AI and simulation techniques offers a new approach to the management of large facilities. In this Bachelor Thesis' a Digital Twin of the Football Club Barcelona flagship sports venue: the Camp Nou has been developed. The most important aspect in the functioning of the venue are pedestrian flows and optimising them, ensuring robust emergency planning and managing change related to phased construction project involving a full renovation of the Camp Nou precinct are the main priorities. This prototype of Pedestrian Movement Model shows the feasibility of combining various data streams to represent multiple scenarios of crowd management. This model will serve as a baseline for integrating data coming from a number of sensors and preprocessed with Machine Learning techniques. This model will be integrated as a part of the whole IoTwins structure of test-beds 5 and 11 that focus on the FCB facilities. The approach taken in the simulation part of the project is Agent Based Modelling. An increasingly popular simulation technique that is able to represent heterogeneous population consisting of individual Agents, for example, representing pedestrians. Their movement is defined with an specially developed algorithm that represent the space around the Agents as a mathematically derived cost function that combines multiple factors affecting the movement of pedestrians. The Agents enter the precinct, move around it and leave it following their independent paths. The model is validated and calibrated using currently available data. Several example scenarios have been run to show the feasibility of the approach for optimising emergency evacuation and construction-caused disruptions to normal operations