MAS-CommonKADS para el desarrollo de un Sistema Multiagente de Información de Recomendación de Rutas de Transporte: SINRUT.

Entre los principales inconvenientes para el uso del transporte público en ciudades como Medellín, está el desconocimiento de las rutas de transporte, así como de los recorridos que estas realizan. Esta necesidad motivó el desarrollo de este trabajo que presenta los aspectos clave de la implementación de un sistema multiagente (SMA) para la recomendación de rutas de transporte en el traslado de un lugar a otro en la ciudad de Medellín. Para el desarrollo de este sistema se utilizaron JADE y Protégé sobre la plataforma JAVA, que se integraron con un módulo de visualización desarrollado en el Framework.NET, extendiendo la funcionalidad del Sistema de Información Geográfica (SIG) de código abierto MapWindow. Las diferentes fases del desarrollo del SMA se llevaron a cabo utilizando la metodología MAS-CommonKADS que será evidenciada en este trabajo con el fin de que sirva de apoyo para el futuro uso de la misma en desarrollos similares.Palabras clave: sistemas multiagentes, algoritmo A*, sistemas de recomendación, MAS-CommonKADS, MapWindow. AbstractThe main drawbacks to the use of public transport in cities like Medellín, is the lack of transportation routes and the routes that they perform. This need motivated the development of this paper presents the key aspects of the implementation of a multi-agent system (MAS) for recommending transport routes in moving from one place to another in the city of Medellin. For the development of the JADE system and Protégé were used on the Java platform, which is integrated with a visualization module developed in the .NET Framework by extending the functionality of Geographic Information System (GIS) open source MapWindow . The different phases of the development of SMA were performed using MAS- CommonKADS methodology that will be evidenced in this work in order to provide back to the future use of the same in similar developments.Keywords: multiagent systems, A* algorithm, recommendation systems, MAS-CommonKADS, MapWindow

Pathfinding algorithms in game development

This review paper provides an overview of a pathfinding algorithm for game development which focuses on the algorithms and their contribution to game development. The algorithms were categorised based on their search performance. The aim of this paper is to investigate and provide insights into pathfinding algorithms for game development in the last 10 years. We summarise all pathfinding algorithms and describe their result in terms of performance (time and memory). The result of this paper is metaheuristic techniques have better performance in terms of time and memory compared to heuristic techniques as a pathfinding algorithm

Hybrid artificial bee colony and flower pollination algorithm for grid-based optimal pathfinding

Pathfinding is essential and necessary for agent movement used in computer games and many other applications. Generally, the pathfinding algorithm searches the feasible shortest path from start to end locations. This task is computationally expensive and consumes large memory, particularly in a large map size. Obstacle avoidance in the game environment increases the complexity to find a new path in the search space. A huge number of algorithms, including heuristic and metaheuristics approaches, have been proposed to overcome the pathfinding problem. Artificial Bee Colony (ABC) is a metaheuristic algorithm that is robust, has fast convergence, high flexibility, and fewer control parameters. However, the best solution founded by the onlooker bee in the presence of constraints is still insufficient and not always satisfactory. A number of variant ABC algorithms have been proposed to achieve the optimal solution. However, it is difficult to simultaneously achieve the optimal solution. Alternatively, Flower Pollination Algorithm (FPA) is one of promising algorithms in optimising problems. The algorithm is easier to implement and faster to reach an optimum solution. Thus, this research proposed Artificial Bee Colony – Flower Pollination Algorithm to solve the pathfinding problem in games, in terms of path cost, computing time, and memory. The result showed that ABC-FPA improved the path cost result by 81.68% and reduced time by 97.84% as compared to the ABC algorithm, which led to a better pathfinding result. This performance indicated that ABC-FPA pathfinding gave better quality pathfinding results

Agent-based simulation of consumer occupancy distribution in shopping centers

Where are the most frequented areas inside a shopping center? The answer to this question can help us find the optimal places to put advertisement, set its price, or even optimize the locations of the stores. This dissertation studies the applicability of simulation techniques to the clients’ circulation inside a shopping center. For that a software capable of integrating the various techniques and showing the results of the simulations is built. We use the agent-based simula-tions where the consumers will be emulated by autonomous entities called agents who have a set of characteristics and behaviors that support their decisions. The navigation of the agents inside the shopping center and the search for the best path is done by the A* algorithm. To show the distribution of the occupancy of the consumers within the shopping center, heat maps are used with a color scale ranging from yellow, meaning less frequented areas, to red, meaning the most frequented areas. This software not only proved to be able to find the most fre-quented areas, but also find them for the various segments of the population, which allow us to know what segment of the population should the advertisement, of the different areas of the shopping center, target.Quais são as áreas mais frequentadas dentro de um centro comercial? A resposta para esta pergunta pode-nos ajudar a encontrar os sítios óptimos para se colocar publicidade, definir o seu custo, e até ajudar a optimizar a localização das lojas. Esta dissertação estuda a aplicabilidade de técnicas de simulação à circulação de clientes dentro de um centro comercial. Para isso é construído um software que seja capaz de integrar as várias técnicas e apresentar os resultados no final das simulações. É usada a simulação baseada por agentes onde os con-sumidores serão emulados por entidades autónomas chamadas de agentes que possuem um conjunto de características e comportamentos que suportam as suas decisões. A navegação dos agentes dentro dos corredores e a procura do melhor caminho para circular é realizada pelo algoritmo A*. Para apresentar a distribuição de ocupação dos consumidores dentro do centro comercial são usa-dos heat maps com uma escala de cores que vai do amarelo, significando zonas menos frequentadas, ao vermelho, significando as zonas mais frequentadas. Este software não só se provou capaz de encontrar as áreas mais frequentadas, como foi capaz de as encontrar para vários segmentos da população, o que nos permite saber qual a população alvo a publicidade deve ter nas diversas áreas do centro comercial

Towards a multilevel ant colony optimization

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman. We have reviewed different elements of multilevel algorithm which helped us in construction of our proposed multilevel ant colony optimization solution. We for comparison purposes implemented our own multi-threaded variant Dijkstra for solving shortest path to compare it with single level and multilevel ant colony optimization and reviewed different techniques such as genetic algorithms and Dijkstra’s algorithm. Our proposed multilevel ant colony optimization was developed based on the single level ant colony optimization which we both implemented. We have applied the novel multilevel ant colony optimization to solve the shortest path and traveling salesman problem. We show that the multilevel variant of ant colony optimization outperforms single level. The experimental results conducted demonstrate the overall performance of multilevel in comparison to the single level ant colony optimization, displaying a vast improvement when employing a multilevel approach in contrast to the classical single level approach. These results gave us a better understanding of the problems and provide indications for further research

Influence-based motion planning algorithms for games

In games, motion planning has to do with the motion of non-player characters (NPCs) from one place to another in the game world. In today’s video games there are two major approaches for motion planning, namely, path-finding and influence fields. Path-finding algorithms deal with the problem of finding a path in a weighted search graph, whose nodes represent locations of a game world, and in which the connections among nodes (edges) have an associated cost/weight. In video games, the most employed pathfinders are A* and its variants, namely, Dijkstra’s algorithm and best-first search. As further will be addressed in detail, the former pathfinders cannot simulate or mimic the natural movement of humans, which is usually without discontinuities, i.e., smooth, even when there are sudden changes in direction. Additionally, there is another problem with the former pathfinders, namely, their lack of adaptivity when changes to the environment occur. Therefore, such pathfinders are not adaptive, i.e., they cannot handle with search graph modifications during path search as a consequence of an event that happened in the game (e.g., when a bridge connecting two graph nodes is destroyed by a missile). On the other hand, influence fields are a motion planning technique that does not suffer from the two problems above, i.e., they can provide smooth human-like movement and are adaptive. As seen further ahead, we will resort to a differentiable real function to represent the influence field associated with a game map as a summation of functions equally differentiable, each associated to a repeller or an attractor. The differentiability ensures that there are no abrupt changes in the influence field, consequently, the movement of any NPC will be smooth, regardless if the NPC walks in the game world in the growing sense of the function or not. Thus, it is enough to have a spline curve that interpolates the path nodes to mimic the smooth human-like movement. Moreover, given the nature of the differentiable real functions that represent an influence field, the removal or addition of a repeller/attractor (as the result of the destruction or the construction of a bridge) does not alter the differentiability of the global function associated with the map of a game. That is to say that, an influence field is adaptive, in that it adapts to changes in the virtual world during the gameplay. In spite of being able to solve the two problems of pathfinders, an influence field may still have local extrema, which, if reached, will prevent an NPC from fleeing from that location. The local extremum problem never occurs in pathfinders because the goal node is the sole global minimum of the cost function. Therefore, by conjugating the cost function with the influence function, the NPC will never be detained at any local extremum of the influence function, because the minimization of the cost function ensures that it will always walk in the direction of the goal node. That is, the conjugation between pathfinders and influence fields results in movement planning algorithms which, simultaneously, solve the problems of pathfinders and influence fields. As will be demonstrated throughout this thesis, it is possible to combine influence fields and A*, Dijkstra’s, and best-first search algorithms, so that we get hybrid algorithms that are adaptive. Besides, these algorithms can generate smooth paths that resemble the ones traveled by human beings, though path smoothness is not the main focus of this thesis. Nevertheless, it is not always possible to perform this conjugation between influence fields and pathfinders; an example of such a pathfinder is the fringe search algorithm, as well as the new pathfinder which is proposed in this thesis, designated as best neighbor first search.Em jogos de vídeo, o planeamento de movimento tem que ver com o movimento de NPCs (“Non-Player Characters”, do inglês) de um lugar para outro do mundo virtual de um jogo. Existem duas abordagens principais para o planeamento de movimento, nomeadamente descoberta de caminhos e campos de influência. Os algoritmos de descoberta de caminhos lidam com o problema de encontrar um caminho num grafo de pesquisa pesado, cujos nós representam localizações de um mapa de um jogo, e cujas ligações (arestas) entre nós têm um custo/peso associado. Os algoritmos de descoberta de caminhos mais utilizados em jogos são o A* e as suas variantes, nomeadamente, o algoritmo de Dijkstra e o algoritmo de pesquisa do melhor primeiro (“best-first search”, do inglês). Como se verá mais adiante, os algoritmos de descoberta de caminhos referidos não permitem simular ou imitar o movimento natural dos seres humanos, que geralmente não possui descontinuidades, i.e., o movimento é suave mesmo quando há mudanças repentinas de direcção. A juntar a este problema, existe um outro que afeta os algoritmos de descoberta de caminhos acima referidos, que tem que ver com a falta de adaptatividade destes algoritmos face a alterações ao mapa de um jogo. Ou seja, estes algoritmos não são adaptativos, pelo que não permitem lidar com alterações ao grafo durante a pesquisa de um caminho em resultado de algum evento ocorrido no jogo (e.g., uma ponte que ligava dois nós de um grafo foi destruída por um míssil). Por outro lado, os campos de influência são uma técnica de planeamento de movimento que não padece dos dois problemas acima referidos, i.e., os campos possibilitam um movimento suave semelhante ao realizado pelo ser humano e são adaptativos. Como se verá mais adiante, iremos recorrer a uma função real diferenciável para representar o campo de influência associado a um mapa de um jogo como um somatório de funções igualmente diferenciáveis, em que cada função está associada a um repulsor ou a um atractor. A diferenciabilidade garante que não existem alterações abruptas ao campo de influência; consequentemente, o movimento de qualquer NPC será suave, independentemente de o NPC caminhar no mapa de um jogo no sentido crescente ou no sentido decrescente da função. Assim, basta ter uma curva spline que interpola os nós do caminho de forma a simular o movimento suave de um ser humano. Além disso, dada a natureza das funções reais diferenciáveis que representam um campo de influência, a remoção ou adição de um repulsor/atractor (como resultado da destruição ou construção de uma ponte) não altera a diferenciabilidade da função global associada ao mapa de um jogo. Ou seja, um campo de influência é adaptativo, na medida em que se adapta a alterações que ocorram num mundo virtual durante uma sessão de jogo. Apesar de ser capaz de resolver os dois problemas dos algoritmos de descoberta de caminhos, um campo de influência ainda pode ter extremos locais, que, se alcançados, impedirão um NPC de fugir desse local. O problema do extremo local nunca ocorre nos algoritmos de descoberta de caminhos porque o nó de destino é o único mínimo global da função de custo. Portanto, ao conjugar a função de custo com a função de influência, o NPC nunca será retido num qualquer extremo local da função de influência, porque a minimização da função de custo garante que ele caminhe sempre na direção do nó de destino. Ou seja, a conjugação entre algoritmos de descoberta de caminhos e campos de influência tem como resultado algoritmos de planeamento de movimento que resolvem em simultâneo os problemas dos algoritmos de descoberta de caminhos e de campos de influência. Como será demonstrado ao longo desta tese, é possível combinar campos de influência e o algoritmo A*, o algoritmo de Dijkstra, e o algoritmo da pesquisa pelo melhor primeiro, de modo a obter algoritmos híbridos que são adaptativos. Além disso, esses algoritmos podem gerar caminhos suaves que se assemelham aos que são efetuados por seres humanos, embora a suavidade de caminhos não seja o foco principal desta tese. No entanto, nem sempre é possível realizar essa conjugação entre os campos de influência e os algoritmos de descoberta de caminhos; um exemplo é o algoritmo de pesquisa na franja (“fringe search”, do inglês), bem como o novo algoritmo de pesquisa proposto nesta tese, que se designa por algoritmo de pesquisa pelo melhor vizinho primeiro (“best neighbor first search”, do inglês)

Agent-based Modelling and Big Data: Applications for Maritime Traffic Analysis

Agent based modeling (ABM) is a powerful tool for examining complex systems in many scientific applications, including maritime transport systems. Growing demands for freight transport and increased industry emphasis on reducing environmental impacts have heightened the focus on vessel and port efficiency. This research aimed to create a maritime route planning model to simulate vessel movement in all waterways. The goal of the ship routing model developed in this research was to develop a simulation tool capable of reproducing real world shipping routes useful for navigation planning, with emphasis on port scheduling and potential application for further use and exploration. A modified breadth-first search algorithm was implemented as a NetLogo ABM in this research. With increasing volumes of ship location monitoring data, new approaches are now possible for examining performance-based metrics and to improve simulations with more precise verification and analysis. A Satellite Automatic Identification System dataset with over 500,000 vessel logs travelling across the Pacific Ocean and into the Port of Metro Vancouver was used as the focal area for model development and validation in this study. Automatic identification system (AIS) is the global standard for maritime navigation and traffic management, and data derived from AIS messages can be used for calibrating simulation model scenarios. In this analysis, the results examined how changes in simulation parameters alter route choice behaviour and how effective large AIS datasets are for validating and calibrating model results. Using large AIS datasets, model results can be quantified to examine how closely they resemble real-time vessels in the same region. Heatmaps provide a data visualization tool that effectively uses large data sets and calculates how closely model results resemble AIS data from the same region. In the case of PMV, the Maritime Ship Routing Model (MSRM) was able to replicate path likeness with a high level of accuracy, generating realistic navigation paths between the many islands on the eastern side of southern Vancouver Island, B.C., a busy marine traffic region and sensitive ecological area. This research highlights the use of ABM as a powerful, user-friendly tool for developing maritime shipping models useful for port scheduling and route analysis. The results of this study emphasize the use of large data sets that are applicable, clean, and reliable as a crucial source for validating and calibrating the MSRM

Application of knowledge based engineering principles to intelligent automation systems

The automation of engineering processes provides many benefits over manual methods including significant cost and scheduling reduction as well as intangible advantages of greater consistency based on agreed methods, standardisation and simplification of complex problems and knowledge retention. Knowledge Based Engineering (KBE) and Design Automation (DA) are two sets of methodologies and technologies for automating engineering processes through software. KBE refers to the structured capture, modelling and deployment of engineering knowledge in high level intelligent systems that provide a wide scope of automation capability. KBE system development is supported by numerous mature methodologies that cover all aspects of the development process including: problem identification and feasibility studies, knowledge capture and modelling, and system design, development and deployment. Conversely, DA is the process of developing automated solutions to specific, well defined engineering tasks. The DA approach is characterised by agile software development methods, producing lower level systems that are intentionally limited in scope. DA-type solutions are more commonly adopted by industry than KBE applications due to shorter development schedules, lower cost and less complex development processes. However, DA application development is not as well supported by theoretical frameworks, and consequently, development processes can be unstructured and best practices not observed. The research presented in this thesis is divided into two key areas. Firstly, a methodology for automating engineering processes is proposed, with the aim of improving the accessibility of mature KBE methods to a broader industrial base. This methodology supports development of automation applications ranging in complexity from high level KBE systems to lower level DA applications. A complexity editing mechanism is introduced that relates detailed processes of KBE methodologies to a set of characteristics that can be exhibited by automated solutions. Depending on individual application requirements, complexity of automated solutions can lowered by deselecting one or more of these characteristics, omitting associated high-level processes from the development methodology. At the lowest level of complexity, the methodology provides a structured process for producing DA applications that incorporates principles of mature KBE methodologies. The second part of this research uses the proposed automation methodology to develop a system to automate the layout design of aircraft electrical harnesses. Increasing complexity of aircraft electrical systems has an associated increase in the number and size of electrical harnesses required to connect subsystems throughout the airframe. Current practices for layout design are highly manual, with many governing rules and best practices. The automation of this process will provide a significant reduction in low level, repetitive, manual work. The resulting automated routing tool implements path-finding techniques from computer game artificial intelligence and microprocessor design domains, together with new methods for incorporating the numerous design rules governing harness placement. The system was tested with a complex industrial test case, and was found to provide harness solutions in a fraction of the time and with comparable quality as equivalent manual design processes. The repeatability of the automated process can also minimise scheduling impacts caused by late design changes