16 research outputs found

    Modelo de estratégia e coordenação genérico para sistemas multi-agente

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    Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Jose Nuno Panelas Nunes LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

    A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing

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    Disasters like terrorist attacks, earthquakes, hurricanes, and volcano eruptions are usually unpredictable events that affect a high number of people. We propose an approach that could be used as a decision support tool for a post-disaster response that allows the assignment of victims to hospitals and organizes their transportation via emergency vehicles. By exploiting the synergy between Mixed Integer Programming and Constraint Programming techniques, we are able to compute the routing of the vehicles so as to rescue much more victims than both heuristic based and complete approaches in a very reasonable time

    RoboCup Rescue Simulation Machine Learning Workshop

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    The Rescue Simulation League is the challenge to learn an optimal response for a team of robots that have to mitigate the effects of a disaster. To operate optimal as teams, several aspects have to be solved at once, such as team formation, task allocation and route planning. This is a hard problem, which could be quite overwhelming for newcomer teams. The proposal is to create a workshop, which demonstrates how each of the aspects could be solved with standard machine learning algorithms, as available in MathWorks' Statistics and Machine Learning Toolbox™

    Proceedings of the 2nd Computer Science Student Workshop: Microsoft Istanbul, Turkey, April 9, 2011

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    Genetic programming for the RoboCup Rescue Simulation System

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    The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting

    Practical and conceptual issues in the use of agent-based modelling for disaster management

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    Application of agent-based modelling technology (ABM) to disaster management has to date been limited in nature. Existing research has concentrated on extending the model structures and agent architectures of complex algorithms to test robustness and extensibility of this simulation approach. Less attention has been brought to bear on testing the current state-of-the-art in ABM for modelling real-life systems. This thesis aims to take first steps in remedying this gap. It focuses on identifying the practical and conceptual issues which preclude wider utilisation of ABM in disaster management. It identifies that insufficient attention is put on incorporating real-life information and domain knowledge into model definitions. This research first proposes a methodology by which some of these issues may be overcome, and consequently tests and evaluates it through implementation of InSiM (Incident Simulation Model), which depicts reaction of pedestrians to a CBRN (chemical, biological, radiological or nuclear) explosion in a city centre. A number of steps are conducted to obtain real-life information related to human response to CBRN incidents. This information is then used for design and parameterisation of InSiM which is implemented in three configurations. In order to identify the effects use of real-life data have on the simulation results each configuration incorporates the information at different level of complexity. The effects are assessed by comparison of the generated dispersion patterns of agents along the city centre. However, use of conventional statistical goodness-of-fit tests for assessing the degree of the difference was challenged by inhomogeneous nature of the data. Hence, alternative approaches are also adopted so that results can be qualitatively assessed. Nevertheless, the evaluation reveals significant differences at global and local level. This research highlights that incorporation of real-life information and domain knowledge into ABM is not without problems. Each time a problem was addressed, additional issues began to emerge. Most of these challenges were related to generalisation of the complex real-life systems that the model represents. Therefore, further investigations are needed at every methodological step before ABM can fully realise its potential to support disaster management

    RoboCup rescue : development of inteligent cooperative agents

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    Mestrado em Engenharia de Computadores e TelemáticaO trabalho desenvolvido nesta dissertação tem como tema o desenvolvimento de um agente inteligente com coordenação e comunicação no ambiente RoboCup Rescue. No RoboCup Rescue existem seis tipos de agentes, no entanto nesta tese só dois agentes foram desenvolvidos, especificamente o tipo de agentes Ambulâncias e Centros de Ambulâncias. O tipo de agente Ambulância é o elemento responsável pelo salvamento de civis na cidade virtual que constitui o ambiente RoboCup Rescue. Para cumprir essa tarefa da forma mais eficiente possível conta com coordenação e comunicação com outros agentes do mesmo tipo, e com os Centros de Ambulâncias. O comportamento da ambulância é modelado tanto para situações em que o Centro de Ambulâncias está presente durante a simulação, podendo, portanto, delegar funções para o Centro; como em situações em que o Centro não está presente, e, por isso, as ambulâncias estão encarregues de todo o processamento dos dados e de todas as tomadas de decisões. As actividades desenvolvidas pelas ambulâncias podem ser resumidas a duas: pesquisa e salvamento. Para a primeira as soluções passam muito pelo uso de algoritmos estudados em Teoria de Grafos, já que a cidade virtual é, na sua essência, um grafo, e são necessárias soluções para problemas como visitar o mapa completamente e determinar o caminho mais rápido entre dois nós. Na parte de salvamento a coordenação tem um grande papel a desempenhar.É necessário determinar que ambulâncias devem ir socorrer que civil, e quantas ambulâncias devem ajudar; ou que ambulâncias que devem continuar com a pesquisa do mapa. Ou seja, a coordenação é vital para uma utilização eficiente dos recursos disponíveis, e, consequentemente, uma boa pontuação. ABSTRACT: The work developed in this thesis has as background the development of an intelligent agent with coordination and communication in the environment of the RoboCup Rescue. RoboCup Rescue has six types of agents, however only two were developed in this thesis, specifically Ambulances and Ambulance Centers. The type of agent Ambulance is the element responsable for the rescuing of civilians in the virtual city which comprises the environment of RoboCup Rescue. To fulfill this task in the most efficient way possible it relies on coordination and communication with other agents of the same type, as well as Ambulance Centers. The behavior of an ambulance is modeled for situations when an Ambulance Center is available during the simulation, thus allowing the ambulances the possibility of dividing some of the processing and decision making; or, for situations when a center is not available and it is up to the ambulances to do make all of the decisions, and do all of the processing. The activities performed by the ambulances can be summarized in two: search, and rescue. For the first, many of the solutions may be provided by algorithms studied in Graph Theory, since the virtual city is, in its essence, a graph, and its necessary solutions to problems such as visit the city entirely, and determine the shortest path between two locations, or nodes. In the rescuing part, the coordination has a very big part to play. It is necessary to choose which ambulances should rescue a civilian, and how many should help doing it; or, which ambulances should continue searching the city for more civilians. In other words, coordination is vital for an efficient allocation of available resources, and, ultimately, a good score

    Managing time budgets shared between planning and execution

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    Agents operating in domains with time budgets shared between planning and execution must carefully balance the need to plan versus the need to act. This is because planning and execution consume the same time resource. Excessive planning can delay the time it takes to achieve a goal, and so reduce the reward attained by an agent. Whereas, insufficient planning will mean the agent creates and executes low reward plans. This thesis looks at three ways to increase the reward achieved by an agent in domains with shared time budgets. The first way is by optimising time allocated to planning, using two different methods -- an optimal plan duration predictor and an online loss limiter. A second is by finding ways to act in a goal-directed manner during planning. We look at using previous plans or new plans generated quickly as heuristics for acting whilst planning. In addition, we present a way of describing actions that are mid-execution to speed the transition between planning and execution. Lastly, this thesis presents a way in which to manage time budgets in multi-agent domains. We use market-based task allocation with deadlines to produce faster task allocation and planning
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