Partitioning Method for Emergent Behavior Systems Modeled by Agent-Based Simulations

Used to describe some interesting and usually unanticipated pattern or behavior, the term emergence is often associated with time-evolutionary systems comprised of relatively large numbers of interacting yet simple entities. A significant amount of previous research has recognized the emergence phenomena in many real-world applications such as collaborative robotics, supply chain analysis, social science, economics and ecology. As improvements in computational technologies combined with new modeling paradigms allow the simulation of ever more dynamic and complex systems, the generation of data from simulations of these systems can provide data to explore the phenomena of emergence. To explore some of the modeling implications of systems where emergent phenomena tend to dominate, this research examines three simulations based on familiar natural systems where each is readily recognized as exhibiting emergent phenomena. To facilitate this exploration, a taxonomy of Emergent Behavior Systems (EBS) is developed and a modeling formalism consisting of an EBS lexicon and a formal specification for models of EBS is synthesized from the long history of theories and observations concerning emergence. This modeling formalism is applied to each of the systems and then each is simulated using an agent-based modeling framework. To develop quantifiable measures, associations are asserted: 1) between agent-based models of EBS and graph-theoretical methods, 2) with respect to the formation of relationships between entities comprising a system and 3) concerning the change in uncertainty of organization as the system evolves. These associations form the basis for three measurements related to the information flow, entity complexity, and spatial entropy of the simulated systems. These measurements are used to: 1) detect the existence of emergence and 2) differentiate amongst the three systems. The results suggest that the taxonomy and formal specification developed provide a workable, simulation-centric definition of emergent behavior systems consistent with both historical concepts concerning the emergence phenomena and modern ideas in complexity science. Furthermore, the results support a structured approach to modeling these systems using agent-based methods and offers quantitative measures useful for characterizing the emergence phenomena in the simulations

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Transport Layer solution for bulk data transfers over Heterogeneous Long Fat Networks in Next Generation Networks

Aquesta tesi per compendi centra les seves contribucions en l'aprenentatge i innovació de les Xarxes de Nova Generació. És per això que es proposen diferents contribucions en diferents àmbits (Smart Cities, Smart Grids, Smart Campus, Smart Learning, Mitjana, eHealth, Indústria 4.0 entre d'altres) mitjançant l'aplicació i combinació de diferents disciplines (Internet of Things, Building Information Modeling, Cloud Storage, Ciberseguretat, Big Data, Internet de el Futur, Transformació Digital). Concretament, es detalla el monitoratge sostenible del confort a l'Smart Campus, la que potser es la meva aportació més representativa dins de la conceptualització de Xarxes de Nova Generació. Dins d'aquest innovador concepte de monitorització s'integren diferents disciplines, per poder oferir informació sobre el nivell de confort de les persones. Aquesta investigació demostra el llarg recorregut que hi ha en la transformació digital dels sectors tradicionals i les NGNs. Durant aquest llarg aprenentatge sobre les NGN a través de les diferents investigacions, es va poder observar una problemàtica que afectava de manera transversal als diferents camps d'aplicació de les NGNs i que aquesta podia tenir una afectació en aquests sectors. Aquesta problemàtica consisteix en el baix rendiment durant l'intercanvi de grans volums de dades sobre xarxes amb gran capacitat d'ample de banda i remotament separades geogràficament, conegudes com a xarxes elefant. Concretament, això afecta al cas d'ús d'intercanvi massiu de dades entre regions Cloud (Cloud Data Sharing use case). És per això que es va estudiar aquest cas d'ús i les diferents alternatives a nivell de protocols de transport,. S'estudien les diferents problemàtiques que pateixen els protocols i s'observa per què aquests no són capaços d'arribar a rendiments òptims. Deguda a aquesta situació, s'hipotetiza que la introducció de mecanismes que analitzen les mètriques de la xarxa i que exploten eficientment la capacitat de la mateixa milloren el rendiment dels protocols de transport sobre xarxes elefant heterogènies durant l'enviament massiu de dades. Primerament, es dissenya l’Adaptative and Aggressive Transport Protocol (AATP), un protocol de transport adaptatiu i eficient amb l'objectiu de millorar el rendiment sobre aquest tipus de xarxes elefant. El protocol AATP s'implementa i es prova en un simulador de xarxes i un testbed sota diferents situacions i condicions per la seva validació. Implementat i provat amb èxit el protocol AATP, es decideix millorar el propi protocol, Enhanced-AATP, sobre xarxes elefant heterogènies. Per això, es dissenya un mecanisme basat en el Jitter Ràtio que permet fer aquesta diferenciació. A més, per tal de millorar el comportament del protocol, s’adapta el seu sistema de fairness per al repartiment just dels recursos amb altres fluxos Enhanced-AATP. Aquesta evolució s'implementa en el simulador de xarxes i es realitzen una sèrie de proves. A l'acabar aquesta tesi, es conclou que les Xarxes de Nova Generació tenen molt recorregut i moltes coses a millorar causa de la transformació digital de la societat i de l'aparició de nova tecnologia disruptiva. A més, es confirma que la introducció de mecanismes específics en la concepció i operació dels protocols de transport millora el rendiment d'aquests sobre xarxes elefant heterogènies.Esta tesis por compendio centra sus contribuciones en el aprendizaje e innovación de las Redes de Nueva Generación. Es por ello que se proponen distintas contribuciones en diferentes ámbitos (Smart Cities, Smart Grids, Smart Campus, Smart Learning, Media, eHealth, Industria 4.0 entre otros) mediante la aplicación y combinación de diferentes disciplinas (Internet of Things, Building Information Modeling, Cloud Storage, Ciberseguridad, Big Data, Internet del Futuro, Transformación Digital). Concretamente, se detalla la monitorización sostenible del confort en el Smart Campus, la que se podría considerar mi aportación más representativa dentro de la conceptualización de Redes de Nueva Generación. Dentro de este innovador concepto de monitorización se integran diferentes disciplinas, para poder ofrecer información sobre el nivel de confort de las personas. Esta investigación demuestra el recorrido que existe en la transformación digital de los sectores tradicionales y las NGNs. Durante este largo aprendizaje sobre las NGN a través de las diferentes investigaciones, se pudo observar una problemática que afectaba de manera transversal a los diferentes campos de aplicación de las NGNs y que ésta podía tener una afectación en estos sectores. Esta problemática consiste en el bajo rendimiento durante el intercambio de grandes volúmenes de datos sobre redes con gran capacidad de ancho de banda y remotamente separadas geográficamente, conocidas como redes elefante, o Long Fat Networks (LFNs). Concretamente, esto afecta al caso de uso de intercambio de datos entre regiones Cloud (Cloud Data Data use case). Es por ello que se estudió este caso de uso y las diferentes alternativas a nivel de protocolos de transporte. Se estudian las diferentes problemáticas que sufren los protocolos y se observa por qué no son capaces de alcanzar rendimientos óptimos. Debida a esta situación, se hipotetiza que la introducción de mecanismos que analizan las métricas de la red y que explotan eficientemente la capacidad de la misma mejoran el rendimiento de los protocolos de transporte sobre redes elefante heterogéneas durante el envío masivo de datos. Primeramente, se diseña el Adaptative and Aggressive Transport Protocol (AATP), un protocolo de transporte adaptativo y eficiente con el objetivo maximizar el rendimiento sobre este tipo de redes elefante. El protocolo AATP se implementa y se prueba en un simulador de redes y un testbed bajo diferentes situaciones y condiciones para su validación. Implementado y probado con éxito el protocolo AATP, se decide mejorar el propio protocolo, Enhanced-AATP, sobre redes elefante heterogéneas. Además, con tal de mejorar el comportamiento del protocolo, se mejora su sistema de fairness para el reparto justo de los recursos con otros flujos Enhanced-AATP. Esta evolución se implementa en el simulador de redes y se realizan una serie de pruebas. Al finalizar esta tesis, se concluye que las Redes de Nueva Generación tienen mucho recorrido y muchas cosas a mejorar debido a la transformación digital de la sociedad y a la aparición de nueva tecnología disruptiva. Se confirma que la introducción de mecanismos específicos en la concepción y operación de los protocolos de transporte mejora el rendimiento de estos sobre redes elefante heterogéneas.This compendium thesis focuses its contributions on the learning and innovation of the New Generation Networks. That is why different contributions are proposed in different areas (Smart Cities, Smart Grids, Smart Campus, Smart Learning, Media, eHealth, Industry 4.0, among others) through the application and combination of different disciplines (Internet of Things, Building Information Modeling, Cloud Storage, Cybersecurity, Big Data, Future Internet, Digital Transformation). Specifically, the sustainable comfort monitoring in the Smart Campus is detailed, which can be considered my most representative contribution within the conceptualization of New Generation Networks. Within this innovative monitoring concept, different disciplines are integrated in order to offer information on people's comfort levels. . This research demonstrates the long journey that exists in the digital transformation of traditional sectors and New Generation Networks. During this long learning about the NGNs through the different investigations, it was possible to observe a problematic that affected the different application fields of the NGNs in a transversal way and that, depending on the service and its requirements, it could have a critical impact on any of these sectors. This issue consists of a low performance operation during the exchange of large volumes of data on networks with high bandwidth capacity and remotely geographically separated, also known as Elephant networks, or Long Fat Networks (LFNs). Specifically, this critically affects the Cloud Data Sharing use case. That is why this use case and the different alternatives at the transport protocol level were studied. For this reason, the performance and operation problems suffered by layer 4 protocols are studied and it is observed why these traditional protocols are not capable of achieving optimal performance. Due to this situation, it is hypothesized that the introduction of mechanisms that analyze network metrics and efficiently exploit network’s capacity meliorates the performance of Transport Layer protocols over Heterogeneous Long Fat Networks during bulk data transfers. First, the Adaptive and Aggressive Transport Protocol (AATP) is designed. An adaptive and efficient transport protocol with the aim of maximizing its performance over this type of elephant network.. The AATP protocol is implemented and tested in a network simulator and a testbed under different situations and conditions for its validation. Once the AATP protocol was designed, implemented and tested successfully, it was decided to improve the protocol itself, Enhanced-AATP, to improve its performance over heterogeneous elephant networks. In addition, in order to upgrade the behavior of the protocol, its fairness system is improved for the fair distribution of resources among other Enhanced-AATP flows. Finally, this evolution is implemented in the network simulator and a set of tests are carried out. At the end of this thesis, it is concluded that the New Generation Networks have a long way to go and many things to improve due to the digital transformation of society and the appearance of brand-new disruptive technology. Furthermore, it is confirmed that the introduction of specific mechanisms in the conception and operation of transport protocols improves their performance on Heterogeneous Long Fat Networks

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

A proactive chatbot framework designed to assist students based on the PS2CLH model

Nowadays, universities are using new technologies to improve the efficiency and effectiveness of learning and to assist students to enhance their academic performance. In fact, for decades, new ways to convey the information required to teach and support students have slowly been integrated into education. This development started decades ago with the popularity of e-mails and the Web. A review of relevant literature revealed that learning requires more innovative and efficient technologies to cope with natural learning challenges, highlighting a need for more effective tools to establish the interaction between humans and machines, lecturers and students. In addition, the covid pandemic presented additional new challenges for the collaboration/interaction of lecturers and students at universities. This situation led to a great demand for such tools. Researchers have been trying to develop such tools for decades, and have made good progress, but they are still in their infancy. There has been a significant evolution in computer hardware in the last decade, leading to advances in AI machine learning and Deep Learning which have made tools such as chatbots more usable. However, the efficiency and effectiveness of the chatbot are still insufficient to meet many educational needs. According to our investigation, current chatbots are mainly based on subject knowledge and therefore assist users with answers which take no consideration of their personal circumstances, which is essential in education. This research aims to design a proactive chatbot framework to assist students. The new chatbot framework integrates students’ learning profiles and subject knowledge, making the chatbot more intelligent to improve student learning and interaction more effectively. The research consists of two main parts. The first part seeks to determine the most effective students’ learning profiles on the basis of the controllable academic factors which affect their performance. The second part develops a chatbot framework to which students’ learning profiles will be applied. Due to the different nature of these two endeavours, a hybrid methodology was used in this research. The literature on learners’ characteristics and the academic factors that affect their performance was reviewed in depth, and this formed the basis for developing a new PS2CLH (psychology, self-responsibility, sociology, communication, learning and health & wellbeing) model on which an individual’s web profile can be built. The PS2CLH model combines the perspectives of psychology, self-responsibility, sociology, communication, learning and health & wellbeing to build a student-controllable learning factor model. This study identifies the impact of students’ controllable factors on their achievement. It was found that the model was 94% accurate. In addition, this research raised participant students’ awareness of PS2CLH perspectives, which helped learners and educators to manage the factors affecting academic performance more effectively. A comprehensive investigation, including a survey, showed that the chatbot supported by AI technology performed better and more efficiently in various assistant situations, including education. However, there is still room for improvement in the effectiveness of the education chatbot. Therefore, the research proposes a new chatbot framework assistant which will integrate students’ learning profiles and develop components to improve student interaction. The new framework uses knowledge from the PS2CLH model AI - Deep Learning to build a proactive chatbot for assisting students’ learning of their academic subjects and their controllable factors that affects students’ performance. One of the principal novelties of the chatbot framework lies in the communication facilitator between student-lecturer/assistant. The proactive chatbot applies multimodality to the students’ learning process to retain their attention and explain the content in different ways using text, image, video and audio to assist the students and improve their learning experience effectively. Furthermore, the chatbot proactively suggests new controllable factors for students to work on, including related factors that influence their academic performance. Tests of the framework showed that the proactive chatbot demonstrated better question response accuracy than the current BERT (Bidirectional Encoder Representations from Transformers) chatbot and presented a more effective learning method for students

A Comprehensive Computational Model of PRIMs Theory for Task-Independent Procedural Learning

The human ability to reason about and learn practically any task has been studied for countless years, but to date we still do not truly understand how human learning is task-independent at the computational level. Researchers have theorized that we can account for many human cognitive behaviors if we combine a task-independent set of primitive procedures with a robust, general learning mechanism that compiles them into cognitive skills for various tasks. The PRIMs theory of procedure learning and transfer is a cognitive architecture theory of human learning that shows how a task-independent set of primitive procedures can support learning in any task that is also supported by the underlying architecture. However, its published architecture implementation, Actransfer, focuses on modeling transfer and does not specify all of the computational details of PRIMs theory. This thesis presents a computationally comprehensive cognitive architecture model of PRIMs theory that I call the PROPs system. I comprehensively define each of the processing steps that PRIMs theory requires and implement these in an agent model using the Soar cognitive architecture. I do this through a methodology for incrementally refining a cognitive architecture model. I use this methodology to extend PRIMs theory and unify it with three-phase learning theory from human performance research, task set theory from psychology and neuroscience, and Soar theory from cognitive architecture research. This achieves several improvements in the model’s ability to replicate human learning behavior. Among the contributions of this work, I introduce a novel form of primitive processing that explains the origins of the primitive procedures of PRIMs theory and supports procedural learning in an unbounded, dynamic working memory space. I show that this improves the model’s ability to match human power-law learning. I also extend Soar cognitive architecture theory with gradual procedural learning in a manner consistent with Soar’s existing theory and introduce a novel computational approach by which a cognitive architecture model can learn to guide automatic long-term declarative memory retrievals based on working memory contents. I finally introduce a novel computational approach by which a model can guide deliberate retrievals through choice-based decision making. In my evaluation of the PROPs system, I identify ways in which PRIMs theory for procedural learning might be further unified with neuroscience theory to broaden the model to include declarative learning. I also identify boundaries where PRIMs models can or cannot currently account for types of human cognitive processing when the models are constrained to be fully task-independent and consistent with the surrounding cognitive architecture. This reveals a path for future cognitive architecture research and development.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169770/1/stearns_1.pd