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
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
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.