Applications of DEC-MDPs in multi-robot systems

International audienceOptimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera.In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We rst describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decen- tralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs

RoboCup rescue : development of inteligent cooperative agents

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

Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief–Desire–Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason