The paper presents a coordinated process through which the situated agents are capable of working jointly using knowledge about their skills to interact with other similar entities. A good coordination is reached because each situated agent is able to represent its suitability rates to perform any action in an effective way. Besides using such rates, an agent can coordinate its actions with other agents. In this sense, each situated agent tries to select and perform only the actions with the highest execution rates. Some experiments in a real robotic soccer test-bed are designed to put in practice the proposed coordinated approach. Final remarks conclude the effectiveness and advantages of the presented work in order to increase the performance of a team composed by situated agents when they must solve complex tasks in a dynamical, competitive and unpredictable scenario.
This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning (CBR) adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.
This paper tackles the problem of forecasting real-life crime. However, the recollected data only produced thirty-five short-sized crime time series for three urban areas. We present a comparative analysis of four simple and four machine-learning-based ensemble forecasting methods. Additionally, we propose five forecasting techniques that manage the seasonal component of the time series. Furthermore, we used the symmetric mean average percentage error and a Friedman test to compare the performance of the forecasting methods and proposed techniques. The results showed that simple moving average with seasonal removal techniques produce the best performance for these series. It is important to highlight that a high percentage of the time series has no auto-correlation and a high level of symmetry, which is deemed as white noise and, therefore, difficult to forecast.
La planeación de cursos de un centro educativo o universidad está compuesta por múltiples problemas complejos como lo es la asignación de horarios para los alumnos, salones y profesores para cada asignatura. Uno de los problemas iniciales es determinar la cantidad de asignaturas que se ofertarán; este problema parece sencillo a simple vista ya que una vez que se tenga la información de la cantidad de alumnos aprobados para cada asignatura, se puede calcular fácilmente la siguiente demanda de asignaturas. Sin embargo, existen ocasiones en los que la planeación de cursos del siguiente período inicia antes de tener la información relativa a la aprobación de los alumnos. Lo cual nos lleva al problema del pronóstico de los porcentajes de aprobación para calcular la demanda de asignaturas de los alumnos. En este trabajo se compara el desempeño de modelos causales contra modelos estadísticos para el pronóstico de los porcentajes de aprobación y reprobación de los alumnos. Los resultados finales muestran una ventaja importante de los métodos causales sobre los métodos estadísticos para los casos de prueba. Consideramos que esta ventaja ocurre debido a que el modelo causal aprende los patrones de comportamiento de los datos de entrenamiento de forma independiente en vez de generalizar porcentajes de acreditación. Además de lo anterior, el método estadístico puede presentar problemas importantes al tratar de pronosticar porcentajes de acreditación para situaciones que no se encuentren en los datos de entrenamiento, mientras que el modelo causal utilizará la información aprendida para pronosticar dichas situaciones.
This research addresses the two-dimensional strip packing problem to minimize the total strip height used, avoiding overlapping and placing objects outside the strip limits. This is an NP-hard optimization problem. We propose a greedy randomized adaptive search procedure (GRASP), incorporating flags as a new approach for this problem. These flags indicate available space after accommodating an object; they hold the available width and height for the following objects. We also propose three waste functions as surrogate objective functions for the GRASP candidate list and use and enhanced selection for the restricted candidate list, limiting the object options to better elements. Finally, we use overlapping functions to ensure that the object fits in the flag because there are some cases where a flag’s width can be wrong due to new object placement. The tests showed that our proposal outperforms the most recent state-of-the-art metaheuristic. Additionally, we make comparisons against two exact algorithms and another metaheuristic.
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