Time granularity in simulation models within a multi-agent system

The understanding of how processes in natural phenomena interact at different scales of time has been a great challenge for humans. How information is transferred across scales is fundamental if one tries to scale up from finer to coarse levels of granularity. Computer simulation has been a powerful tool to determine the appropriate amount of detail one has to impose when developing simulation models of such phenomena. However, it has proved difficult to represent change at many scales of time and subject to cyclical processes. This issue has received little attention in traditional AI work on temporal reasoning but it becomes important in more complex domains, such as ecological modelling. Traditionally, models of ecosystems have been developed using imperative languages. Very few of those temporal logic theories have been used for the specification of simulation models in ecology. The aggregation of processes working at different scales of time is difficult (sometimes impossible) to do reliably. The reason is because these processes influence each other, and their functionality does not always scale to other levels. Thus the problems to tackle are representing cyclical and interacting processes at many scales and providing a framework to make the integration of such processes more reliable. We propose a framework for temporal modelling which allows modellers to represent cyclical and interacting processes at many scales. This theory combines both aspects by means of modular temporal classes and an underlying special temporal unification algorithm. To allow integration of different models they are developed as agents with a degree of autonomy in a multi-agent system architecture. This Ecoagency framework is evaluated on ecological modelling problems and it is compared to a formal language for describing ecological systems

NatureTime: Temporal Granularity in Simulation of Ecosystems

AbstractGranularity of time is an important issue for the understanding of how actions performed at coarse levels of time interact with others, working at finer levels. However, it has not received much attention from most AI work on temporal logic. In simpler domains of application we may not need to consider it a problem but it becomes important in more complex domains, such as ecological modelling. In this domain, aggregation of processes working at different time granularities (and sometimes cyclically) is very difficult to achieve reliably. We have proposed a new time granularity theory based onmodular temporal classes, and have developed a temporal reasoning system to specify cyclical processes of simulation models in ecology at many levels of time

[Photograph 2012.201.B1203.0708]

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