From naive to sophisticated behavior in multiagents based financial market models

We discuss the behavior of two magnitudes, physical complexity and mutual information function of the outcome of a model of heterogeneous, inductive rational agents inspired in the El Farol Bar problem and the Minority Game. The first is a measure rooted in Kolmogorov-Chaitin theory and the second one a measure related with information entropy of Shannon. We make extensive computer simulations, as result of which, we propose an ansatz for physical complexity and establish the dependence of exponent of that ansatz from the parameters of the model. We discuss the accuracy of our results and the relationship with the behavior of mutual information function as a measure of time correlations of agents choice.Comment: 16 pages, 4 figures, submitted to Physica

Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim

Cloud computing has been widely accepted by the researchers for the web applications. During the past years, distributed computing replaced the centralized computing and finally turned towards the cloud computing. One can see lots of applications of cloud computing like online sale and purchase, social networking web pages, country wide virtual classes, digital libraries, sharing of pathological research labs, supercomputing and many more. Creating and allocating VMs to applications use virtualization concept. Resource allocates policies and load balancing polices play an important role in managing and allocating resources as per application request in a cloud computing environment. Cloud analyst is a GUI tool that simulates the cloud-computing environment. In the present work, the cloud servers are arranged through step network and a UML model for a minimization of energy consumption by processor, dynamic random access memory, hard disk, electrical components and mother board is developed. A well Unified Modeling Language is used for design of a class diagram. Response time and internet characteristics have been demonstrated and computed results are depicted in the form of tables and graphs using the cloud analyst simulation tool

A Survey on Dynamic Spectrum Access Techniques in Cognitive Radio Networks

The idea of Cognitive Radio (CR) is to share the spectrum between a user called primary, and a user called secondary. Dynamic Spectrum Access (DSA) is a new spectrum sharing paradigm in cognitive radio that allows secondary users to access the abundant spectrum holes in the licensed spectrum bands. DSA is an auspicious technology to alleviate the spectrum scarcity problem and increase spectrum utilization. While DSA has attracted many research efforts recently, in this paper, a survey of spectrum access techniques using cooperation and competition to solve the problem of spectrum allocation in cognitive radio networks is presented

The Immersive Media Library @ VCU

Answering the call issued by John Underkoffler in 2010 about the future of UI, I have imagined the Immersive Media Library (IML) as an annex of the main VCU library, offering a concentration of visually immersive spaces to compliment the space the university is already building in the renovated Cabell Library. The design is new in that the emphasis is placed on the collaboration between librarians and visitors in creating new work. Focusing on the interpersonal might be unexpected from program with such an emphasis on new technology - but I see it as vital part of the new computing paradigm

Zero-gravity movement studies

The use of computer graphics to simulate the movement of articulated animals and mechanisms has a number of uses ranging over many fields. Human motion simulation systems can be useful in education, medicine, anatomy, physiology, and dance. In biomechanics, computer displays help to understand and analyze performance. Simulations can be used to help understand the effect of external or internal forces. Similarly, zero-gravity simulation systems should provide a means of designing and exploring the capabilities of hypothetical zero-gravity situations before actually carrying out such actions. The advantage of using a simulation of the motion is that one can experiment with variations of a maneuver before attempting to teach it to an individual. The zero-gravity motion simulation problem can be divided into two broad areas: human movement and behavior in zero-gravity, and simulation of articulated mechanisms

Design of a strategy to obtain safe paths from collaborative robot teamwork

Documento en PDF a color.figuras, tablasThis doctoral thesis was designed and implemented using a strategy of explorer agents and a management and monitoring system to obtain the shortest and safest paths. The strategy was simulated using Matlab R2016 in 10 test environments. The comparisons were made between the results obtained by considering each robot's work and contrasting it with the results obtained by implementing the cooperative-collaborative strategy. For this purpose, were used two path planning algorithms, they are the A* and the Greedy Best First Search (GBFS). Some changes were made to these classic algorithms to improve their performance to guarantee interactions and comparisons between them, transforming them into Incremental Heuristic (IH) algorithms, which gave rise to a couple of agents with new path planners called IH-A* and IH-GBFS. The cooperative strategy was implemented with IH-A* and IH-GBFS algorithms to obtain the shortest paths. The cooperative process was used 300 times in 100 complete tests (3 times in 10 tests in each of 10 environments), which allowed determining that the strategy decreased the original path (without cooperation) in 79% of the cases. In 20.50% of cases, the author identified that the cooperative process, reduced to less than half the original path. The collaborative strategy was implemented to obtain the safer path, using a communications system that allows the interaction among the explorer agents, the test environment, and the management and monitoring system to generate early warnings and compare the risk between paths. In this work, the risk is due to hidden marks found by the explorer agents; for this reason, it is implemented a potential risk function that allows obtaining the path risk estimated. The path risk estimated metric is the one that facilitates the evaluation and comparison of risk between paths to find safer paths. The AWMRs operates using a kinematic model, a controller, a path planner, and sensors that allow them to navigate through the environment gently and safely. Simultaneously with the explorer agents, the administration and monitoring system as a user interface that facilitates the presentation and consolidation of results were implemented. Subsequently, 16 tests were carried out, implementing the complete cooperative-collaborative strategy in four different environments, which had hidden marks. When analyzing the results, it was determined that the Shortest Safest Estimated Path was found in 62.5% of the tests. A WMR and a square test stage were built. In the test scenario, 240 path tracking tests were carried out (the WMR travelled 24 different paths; the WMR travelled each path ten times). The path data were obtained using odometry with encoders onboard the robot and image processing through an external camera. The author apply a tracking error analysis on the WMR path, travelling a circumference of 3.64 m in length. When comparing the path obtained with the WMR kinematic model with the data obtained using image processing, a Mean Absolute Percentage Error (MAPE) of 2,807% was obtained; and with the odometry data, the MAPE was 1,224%. As a general conclusion, this study has numerically identified the relevance of the implementation of the cooperative-collaborative strategy in robotic teamwork to find shortest and safest paths, a strategy applied in test environments that have obstacles and hidden marks. The cooperative-collaborative strategy can be used in different applications that involve displacement in a dangerous place or environment, such as a minefield or a region at risk of spreading COVID-19.Esta tesis doctoral fue diseñada e implementada utilizando una estrategia de agentes exploradores y un sistema de gestión y seguimiento para obtener caminos más cortos y seguros. La estrategia se simuló utilizando Matlab R2016 en 10 entornos de prueba. Las comparaciones se realizaron entre los resultados obtenidos al considerar el trabajo realizado por cada robot y contrastarlo con los resultados obtenidos al implementar la estrategia cooperativa-colaborativa. Para ello, se utilizaron dos algoritmos de planificación de rutas, que son el A* y el Greedy Best First Search (GBFS). Se realizaron algunos cambios a estos algoritmos clásicos para mejorar su rendimiento para garantizar interacciones y comparaciones entre ellos, transformándolos en algoritmos Heurísticos Incrementales (IH), lo que dio lugar a un par de agentes con nuevos planificadores de rutas denominados IH-A * e IH- GBFS. La estrategia cooperativa se implementó con algoritmos IH-A * e IH-GBFS para obtener los caminos más cortos. El proceso cooperativo se utilizó 300 veces en 100 pruebas completas (3 veces en 10 pruebas en cada uno de los 10 entornos), lo que permitió determinar que la estrategia disminuyó la trayectoria original (sin cooperación) en el 79% de los casos. En el 20,50% de los casos, el autor identificó que el proceso cooperativo, redujo la distancia entre inicio y meta a menos de la mitad del recorrido original. La estrategia colaborativa se implementó para obtener el camino más seguro, utilizando un sistema de comunicaciones que permite la interacción entre los agentes exploradores, el entorno de prueba y el sistema de gestión y monitoreo para generar alertas tempranas y comparar el riesgo entre caminos. En este trabajo, el riesgo se debe a las marcas ocultas encontradas por los agentes exploradores; por ello, se implementa una función de riesgo potencial que permite obtener el riesgo de ruta estimado. La métrica estimada de riesgo de ruta es la que facilita la evaluación y comparación de riesgo entre rutas para encontrar rutas más seguras. Los robots autónomos móviles con ruedas (en inglés AWMR) operan utilizando un modelo cinemático, un controlador, un planificador de rutas y sensores que les permiten navegar por el entorno de manera suave y segura. Simultáneamente con los agentes exploradores, el autor implementó un sistema de administración y monitoreo como interfaz de usuario que facilita la presentación y consolidación de resultados. Posteriormente, se realizaron 16 pruebas, implementando la estrategia cooperativa-colaborativa completa en cuatro entornos diferentes, que tenían marcas ocultas. Al analizar los resultados, se determinó que una ruta estimada más corta y más segura se obtenía en el 62.5% de las pruebas. Se construyeron un WMR y un escenario de prueba cuadrado. En el escenario de prueba, se llevaron a cabo 240 pruebas de seguimiento de ruta (el WMR recorrió 24 rutas diferentes; el WMR recorrió cada ruta diez veces). Los datos de la trayectoria se obtuvieron utilizando odometría con encoders a bordo del robot y procesamiento de imágenes a través de una cámara externa. El autor aplica un análisis de error de seguimiento en la ruta recorrida por el WMR, generando una circunferencia de 3,64 m de longitud. Al comparar la ruta obtenida con el modelo cinemático del WMR con los datos obtenidos usando el procesamiento de imágenesse obtuvo un error de porcentaje absoluto medio (MAPE) de 2.807%; y con los datos de odometría, el MAPE fue de 1,224%. Como conclusión general, este estudio ha identificado numéricamente la relevancia de la implementación de la estrategia cooperativa-colaborativa en el trabajo en equipo robótico para encontrar caminos más cortos y seguros, estrategia aplicada en entornos de prueba que poseen obstáculos y marcas ocultas. La estrategia cooperativa-colaborativa puede ser utilizada en diferentes aplicaciones que involucran el desplazamiento en un lugar o entorno peligroso, como pueden ser un campo minado o una región en riesgo de propagación de COVID-19.DoctoradoDoctor en Ingeniería - Ingeniería Automátic

8th International Conference on Practical Applications of Agents and Multiagent Systems

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an international yearly forum to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their ex-perience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2010 edition in the Special Sessions and Workshops. PAAMS'10 Special Sessions and Workshops are a very useful tool in order to complement the regular program with new or emerging topics of particular interest to the participating community. Special Ses-sions and Workshops that emphasize on multi-disciplinary and transversal aspects, as well as cutting-edge topics were especially encouraged and welcomed

Satisfaction-Aware Data Offloading in Surveillance Systems

In this thesis, exploiting Fully Autonomous Aerial Systems\u27 (FAAS) and Mobile Edge Computing (MEC) servers\u27 computing capabilities to introduce a novel data offloading framework to support the energy and time-efficient video processing in surveillance systems based on satisfaction games. A surveillance system is introduced consisting of Areas of Interest (AoIs), where a MEC server is associated with each AoI, and a FAAS is flying above the AoIs to support the IP cameras\u27 computing demands. Each IP camera adopts a utility function capturing its Quality of Service (QoS) considering the experienced time and energy overhead to offload and process remotely or locally the data. A non-cooperative game among the cameras is formulated to determine the amount of offloading data to the MEC server and/or the FAAS, and the novel concept of Satisfaction Equilibrium (SE) is introduced where the IP cameras satisfy their minimum QoS prerequisites instead of maximizing their performance by consuming additional system resources. A distributed learning algorithm determines the IP cameras\u27 stable data offloading. Also, a reinforcement learning algorithm indicates the FAAS\u27s movement among the AoIs exploiting the accuracy, timeliness, and certainty of the collected data by the IP cameras per AoI. Detailed numerical and comparative results are presented to show the operation and efficiency of the proposed framework