Abstract
In researches of multi-agent systems, evaluation method for multi-agent systems is one of important problems. Especially, evaluating agents, that behave on various environment, is difficult. In this paper, we analyze and clarify the relationship between map features and the evaluation of multi-agent systems in RoboCupRescue simulation. Through this paper, we can define evaluation indicators for each multi-agent system. And it makes it easy to compare one multi-agent system with the others on different environment. In other words, our final goal is to analyze evaluation values of some (rescue) agents'activity on an affected area and compare them. Besides, contribution of this paper is for complicated problem like disaster simulation, although traditional research is for simple problem like moving (or walking) simulation. In this point, we can regard this paper as a practical and important work. At first, we extract map features from a map. And then the features are quantified based on network analysis, urban engineering and so on. Road networks and building locations are adopted as such map features. Because rescue activity result depends on the road connectivity and building density. Therefore the features are defined as 5 classifications-building area, building element, location relationship among buildings, location relationship among buildings and roads, and density. And the score of the RoboCupRescue simulation is adopted as evaluation value of agents. Because therescue simulation includes many heterogeneous agents (firebrigades, ambulance team, and policeforce), integrated value is required to evaluate such agents. Before analyzing relationships between the map features and the evaluation values of the agents, it needs to remove multicollinearity from the defined the map features. And the results are analyzed based on partial correlation analysis. Through the analysis, we confirmed that the evaluation values of the multi-agent systems in RoboCupRescue simulation depend on the map features and that each algorithm of multi-agent systems has distinct dependency for the map features.
