Cooperación en sistemas distribuidos de robots reactivos minimizando la cantidad de información comunicada

Actas de: Simposio Español de Infomática Distribuida (SEID 2000), Ourense, 25-27 de septiembre de 2000La coordinación emergente pretende obtener comportamientos colaborativos entre diversos agentes sin que eso implique que cada individuo deba tener un conocimiento global del dominio, y sin que ese conocimiento deba estar centralizado. Al no requerir conocimiento global, se minimiza la comunicación entre los agentes de forma que cada uno de ellos puede comportarse de forma reactiva y totalmente autónoma. En este trabajo se presenta una primera aproximación a este modelo de coordinación aplicado al dominio de la RoboCup.Publicad

Programming Robosoccer agents by modelling human behavior

The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.Publicad

Roboskeleton: an architecture for coordinating Robot Soccer agents

SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi-agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework.Publicad

Multi-agent architecture for intelligent gathering systems

This paper presents a model to define heterogeneous agents that solve problems by sharing the knowledge retrieved from the WEB, and cooperating among them. The control structure of those agents is based on a general purpose Multi-Agent architecture (SKELETONAGENT) based on a deliberative approach. Any agent in the architecture is built by means of several interrelated modules: control module, language and communication module, skills modules, knowledge base, yellow pages, etc. The control module uses an agenda to activate and coordinate the agent skills. This agenda handles actions from both the internal goals of the agent and from other agents in the environment. In the paper, we show a high level agent model, which is later instantiated to build a set of heterogeneous specialized agents. The paper describes how SKELETONAGENT has been used to implement different kinds of agents and a specialized Multi-Agent System (MAS). The implemented MAS, MAPWEB-ETOURISM, is the specific implementation of a general WEB gathering architecture, named MAPWEB, which extends SKELETONAGENT. MAPWEB has been designed to solve problems in WEB domain through the integration of information gathering and planning techniques. The MAPWEB-ETOURISM system has been applied to a specific WEB domain (e-tourism) which uses information gathered directly from several WEB sources (plane, train, and hotel companies) to solve travel problems. This paper shows how the proposed architecture allows to integrate the different agents tasks with AI techniques like planning to build a MAS which is able to gather and integrate information retrieved from the WEB to solve problems.Publicad