Abstract
There is considerable interest in the simulation of systems where humans move around, for example for traffic or pedestrian simulations. Any such simulation consists of two layers: the simulation of the “physical” system, which includes effects such as interaction with other agents or the environment; and the simulation of the “mental” layer, which generates strategies of the agents. The traditional way to couple the modules is to use files. The disadvantage of that approach is twofold: The computational performance is limited by I/O; and the modules can only be run sequentially.
In order to overcome these problems without sacrificing modularity, a message-based approach is presented. Agent strategies are sent via messages to the simulation of the physical system, which executes them and sends back performance information in the form of “events”. The strategic modules listen to these events, memorize them in some appropriate way, and possibly generate revised strategies. These strategies are sent to the simulation of the physical system immediately, so that the representation of the agent in the physical system will switch to the new strategy right away.
In addition, the same messages can also be used to plug helper modules, such as viewers or recorders, into the system. An implementation of the framework is tested within our project, which explores the feasibility of using autonomous agent modeling to evaluate future scenarios in a tourist landscape in the Swiss Alps.
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Gloor, C., Cavens, D., Nagel, K. (2005). A Message-Based Framework for Real-World Mobility Simulations. In: Klügl, F., Bazzan, A., Ossowski, S. (eds) Applications of Agent Technology in Traffic and Transportation. Whitestein Series in Software Agent Technologies. Birkhäuser Basel. https://doi.org/10.1007/3-7643-7363-6_13
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DOI: https://doi.org/10.1007/3-7643-7363-6_13
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