Modeling, Evaluation, and Scale on Artificial Pedestrians: A Literature Review

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this article is to provide a bibliographic outlook so that the reader may have quick access to the most relevant works related to this problem. We have used three main axes to organize the article's contents: pedestrian models, validation techniques, and multiscale approaches. The backbone of this work is the classification of existing pedestrian models; we have organized the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then the main existing validation methods, oriented to evaluate the behavioral quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we first focus on the behavioral scale (combinations of micro and macro pedestrian models) and second on the scale size (from individuals to crowds). The article begins by introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can make in the near future to this exciting area

Automated Discovery of Candidate Simulation Models for Steering Behavior Simulation

Steering behavior of autonomous agents plays important roles in many simulation applications, such as simulation of pedestrian crowds, simulation of evacuation scenarios, simulation of ecosystems, simulation of autonomous robots, and simulation of artificial life in virtual environments used in computer games. It is desirable to have an approach that can automatically discover multiple candidate models for steering behavior simulation besides manual approach (trial-and-error fashion) and data-driven approach. Towards this goal, this work presents an approach that searches for candidate models of steering behavior in an automated way. The proposed framework includes two components. A model space specification provides a formal specification for a general structure from which various models can be constructed, and a search method to search for a set of candidate models based on requirements. To support more complex scenarios, we further add three major extensions including: (1) Activation component assign dynamic priorities for behaviors depending on surround environments. (2) Multiple search stages are provided to assist the evolutionary search algorithm to distribute computational resources better. (3) A special type of entity called space entity to assist agents receive information not only from other entities (agents, obstacles), but also from surrounding empty space. The approach is able to discover multiple candidate models for three basic steering behaviors including the leader- following ( Bleader_following), personal space maintenance ( Bpersonal_space), and mobile obstacle avoidance ( Bobstacle_avoidance). The results show that different possibilities of steering behavior support modelers to have a better understanding of the problem under study, hence assist modelers to develop more advanced models by testing different combinations of the basic steering behaviors. We evaluate all combinations between three basic steering behaviors including: (1) Bleader_following + Bobstacle_avoidance, (2) Bobstacle_avoidance + Bpersonal_space, (3) Bleader_following + Bpersonal_space, and (4) Bleader_following + Bobstacle_avoidance + Bpersonal_space. We further test the approach with two variations of scenario 4: (5) The leader surrounding + Bpersonal_space, (6) Hall-way evacuation with an obstacle in the middle. The results show that the framework is also able to discover multiple models for each of these composite steering behaviors, and several of them have good scalability and robustness

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

Modeling social norms in real-world agent-based simulations

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans\u27 actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing

Reinforcement learning in a multi-agent framework for pedestrian simulation

El objetivo de la tesis consiste en la utilización de Aprendizaje por refuerzo (Reinforcement Learning) para generar simulaciones plausibles de peatones en diferentes entornos. Metodología Se ha desarrollado un marco de trabajo multi-agente donde cada agente virtual que aprende un comportamiento de navegación por interacción con el mundo virtual en el que se encuentra junto con el resto de agentes. El mundo virtual es simulado con un motor físico (ODE) que está calibrado con parámetros de peatones humanos extraídos de la bibliografía de la materia. El marco de trabajo es flexible y permite utilizar diferentes algoritmos de aprendizaje (en concreto Q-Learning y Sarsa(lambda) en combinación con diferentes técnicas de generalización del espacio de estados (en concreto cuantización Vectorial y tile coding). Como herramientas de análisis de los comportamientos aprendidos se utilizan diagramas fundamentales (relación velocidad/densidad), mapas de densidad, cronogramas y rendimientos (en términos del porcentaje de agentes que consiguen llegar al objetivo). Conclusiones: Tras una batería de experimentos en diferentes escenarios (un total de 6 escenarios distintos) y los correspondientes analisis de resultados, las conclusiones son las siguientes: - Se han conseguido comportamientos plausibles de peatones -Los comportamientos son robustos al escalado y presentan capacidades de abstracción (comportamientos a niveles táctico y de planificación) -Los comportamientos aprendidos son capaces de generar comportamientos colectivos emergentes -La comparación con otro modelo de peatones estandar (Modelo de Helbing) y los análisis realizados a nivel de diagramas fundamentales, indican que la dinámica aprendida es coherente y similar a una dinámica de peatones

Social Emotions in Multiagent Systems

Tesis por compendioA lo largo de los últimos años, los sistemas multi-agente (SMA) han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos. Este potencial no se debe principalmente a sus características individuales (como son su autonomía, su capacidad de percepción, reacción y de razonamiento), sino que también a la capacidad de comunicación y cooperación a la hora de conseguir un objetivo. De hecho, su capacidad social es la que más llama la atención, es este comportamiento social el que dota de potencial a los sistemas multi-agente. Estas características han hecho de los SMA, la herramienta de inteligencia artificial (IA) más utilizada para el diseño de entornos virtuales inteligentes (IVE), los cuales son herramientas de simulación compleja basadas en agentes. Sin embargo, los IVE incorporan restricciones físicas (como gravedad, fuerzas, rozamientos, etc.), así como una representación 3D de lo que se quiere simular. Así mismo, estas herramientas no son sólo utilizadas para la realización de simulaciones. Con la aparición de nuevas aplicaciones como \emph{Internet of Things (IoT)}, \emph{Ambient Intelligence (AmI)}, robot asistentes, entre otras, las cuales están en contacto directo con el ser humano. Este contacto plantea nuevos retos a la hora de interactuar con estas aplicaciones. Una nueva forma de interacción que ha despertado un especial interés, es el que se relaciona con la detección y/o simulación de estados emocionales. Esto ha permitido que estas aplicaciones no sólo puedan detectar nuestros estados emocionales, sino que puedan simular y expresar sus propias emociones mejorando así la experiencia del usuario con dichas aplicaciones. Con el fin de mejorar la experiencia humano-máquina, esta tesis plantea como objetivo principal la creación de modelos emocionales sociales, los cuales podrán ser utilizados en aplicaciones MAS permitiendo a los agentes interpretar y/o emular diferentes estados emocionales y, además, emular fenómenos de contagio emocional. Estos modelos permitirán realizar simulaciones complejas basadas en emociones y aplicaciones más realistas en dominios como IoT, AIm, SH.Over the past few years, multi-agent systems (SMA) have proven to be a powerful and versatile paradigm, with great potential for solving complex problems in dynamic and distributed environments. This potential is not primarily due to their individual characteristics (such as their autonomy, their capacity for perception, reaction and reasoning), but also the ability to communicate and cooperate in achieving a goal. In fact, its social capacity is the one that draws the most attention, it is this social behavior that gives potential to multi-agent systems. These characteristics have made the SMA, the artificial intelligence (AI) tool most used for the design of intelligent virtual environments (IVE), which are complex agent-based simulation tools. However, IVE incorporates physical constraints (such as gravity, forces, friction, etc.), as well as a 3D representation of what you want to simulate. Also, these tools are not only used for simulations. With the emergence of new applications such as \emph {Internet of Things (IoT)}, \emph {Ambient Intelligence (AmI)}, robot assistants, among others, which are in direct contact with humans. This contact poses new challenges when it comes to interacting with these applications. A new form of interaction that has aroused a special interest is that which is related to the detection and / or simulation of emotional states. This has allowed these applications not only to detect our emotional states, but also to simulate and express their own emotions, thus improving the user experience with those applications. In order to improve the human-machine experience, this thesis aims to create social emotional models, which can be used in MAS applications, allowing agents to interpret and / or emulate different emotional states, and emulate phenomena of emotional contagion. These models will allow complex simulations based on emotions and more realistic applications in domains like IoT, AIm, SH.Al llarg dels últims anys, els sistemes multi-agent (SMA) han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexos en entorns dinàmics i distribuïts. Aquest potencial no es deu principalment a les seues característiques individuals (com són la seua autonomia, la seua capacitat de percepció, reacció i de raonament), sinó que també a la capacitat de comunicació i cooperació a l'hora d'aconseguir un objectiu. De fet, la seua capacitat social és la que més crida l'atenció, és aquest comportament social el que dota de potencial als sistemes multi-agent. Aquestes característiques han fet dels SMA, l'eina d'intel·ligència artificial (IA) més utilitzada per al disseny d'entorns virtuals intel·ligents (IVE), els quals són eines de simulació complexa basades en agents. No obstant això, els IVE incorporen restriccions físiques (com gravetat, forces, fregaments, etc.), així com una representació 3D del que es vol simular. Així mateix, aquestes eines no són només utilitzades per a la realització de simulacions. Amb l'aparició de noves aplicacions com \emph{Internet of Things (IOT)}, \emph{Ambient Intelligence (AmI)}, robot assistents, entre altres, les quals estan en contacte directe amb l'ésser humà. Aquest contacte planteja nous reptes a l'hora d'interactuar amb aquestes aplicacions. Una nova forma d'interacció que ha despertat un especial interès, és el que es relaciona amb la detecció i/o simulació d'estats emocionals. Això ha permès que aquestes aplicacions no només puguen detectar els nostres estats emocionals, sinó que puguen simular i expressar les seues pròpies emocions millorant així l'experiència de l'usuari amb aquestes aplicacions. Per tal de millorar l'experiència humà-màquina, aquesta tesi planteja com a objectiu principal la creació de models emocionals socials, els quals podran ser utilitzats en aplicacions MAS permetent als agents interpretar i/o emular diferents estats emocionals i, a més, emular fenòmens de contagi emocional. Aquests models permetran realitzar simulacions complexes basades en emocions i aplicacions més realistes en dominis com IoT, AIM, SH.Rincón Arango, JA. (2018). Social Emotions in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/98090TESISCompendi

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c