Agents in Biology
183
food distribution, thereby allowing more individuals in a group to benefit
by successfully locating food finds [164, 97, 96].
Social insects: soil disposal organization. Colonies of Pheidole ambigua
ants excavate soil and drop it outside the nest entrance. The deposition
of thousands of loads leads to the formation of regular ring-shaped piles.
But, how is this pattern generated?
This study investigated soil pile formation on level and sloping surfaces,
both empirically and using an agent-based model. The authors found
that ants drop soil preferentially in the direction in which the slope is
least steeply uphill from the nest entrance, both when adding to an existing pile and when starting a new pile. Ants respond to cues from local
slopes to choose downhill directions. Ants walking on a slope increase
the frequency and magnitude of changes in direction, and more of these
changes of direction take them downhill than uphill. Also, ants carrying soil on a slope wait longer before dropping their soil compared to
ants on a level plane. These mechanisms combine to focus soil dropping
in the downhill direction, without the necessity of a direct relationship
between slope and probability of dropping soil. These empirically determined rules were used to simulate soil disposal. The slight preference
for turning downhill measured empirically was shown in the model to
be sufficient to generate biologically realistic patterns of soil dumping
when combined with memory of the direction of previous trips. From
simple rules governing individual behavior, an overall pattern emerges,
which is appropriate to the environment and allows a rapid response to
changes [163].
Further general titles and experiments can be found in [86, 87].
7.1.4 Industrial Applications of Agent-Based Modeling with
FLAME
Active management of crowds in airports, stations and shopping malls.
This used the Concoursia software application, based on FLAME-GPU.
This models individual people moving around precisely modeled buildings, used for both planning buildings and also for actively managing
crowds when connected to suitable sensor systems. This provides managers with a decision support tool for making decisions about potential
interventions to deal with overcrowding based on predictions provided
by the FLAME model (Figure 7.1(a)).
Managing patient demand in hospital A&E departments. This uses
the application PatientFlow, based on FLAME running on an HPC grid.
In the model, each patient, staff member and ancillary service is modeled
as individuals, to present a detailed representation of how the hospital
183
food distribution, thereby allowing more individuals in a group to benefit
by successfully locating food finds [164, 97, 96].
Social insects: soil disposal organization. Colonies of Pheidole ambigua
ants excavate soil and drop it outside the nest entrance. The deposition
of thousands of loads leads to the formation of regular ring-shaped piles.
But, how is this pattern generated?
This study investigated soil pile formation on level and sloping surfaces,
both empirically and using an agent-based model. The authors found
that ants drop soil preferentially in the direction in which the slope is
least steeply uphill from the nest entrance, both when adding to an existing pile and when starting a new pile. Ants respond to cues from local
slopes to choose downhill directions. Ants walking on a slope increase
the frequency and magnitude of changes in direction, and more of these
changes of direction take them downhill than uphill. Also, ants carrying soil on a slope wait longer before dropping their soil compared to
ants on a level plane. These mechanisms combine to focus soil dropping
in the downhill direction, without the necessity of a direct relationship
between slope and probability of dropping soil. These empirically determined rules were used to simulate soil disposal. The slight preference
for turning downhill measured empirically was shown in the model to
be sufficient to generate biologically realistic patterns of soil dumping
when combined with memory of the direction of previous trips. From
simple rules governing individual behavior, an overall pattern emerges,
which is appropriate to the environment and allows a rapid response to
changes [163].
Further general titles and experiments can be found in [86, 87].
7.1.4 Industrial Applications of Agent-Based Modeling with
FLAME
Active management of crowds in airports, stations and shopping malls.
This used the Concoursia software application, based on FLAME-GPU.
This models individual people moving around precisely modeled buildings, used for both planning buildings and also for actively managing
crowds when connected to suitable sensor systems. This provides managers with a decision support tool for making decisions about potential
interventions to deal with overcrowding based on predictions provided
by the FLAME model (Figure 7.1(a)).
Managing patient demand in hospital A&E departments. This uses
the application PatientFlow, based on FLAME running on an HPC grid.
In the model, each patient, staff member and ancillary service is modeled
as individuals, to present a detailed representation of how the hospital