The agent step() function has been further improved.
Now, ants behave as roamers and follow a positive gradient in
pheromones until they find food, when they find food they will switch
what chemical they are dropping (A -> B) and will look for A.
When they return they will recruit new ants.
Currently, the sensitivity to the pheromone concentration is clamped
linear, meaning that:
{ 0 if concentration < lower_threshold
sens(concentration) = { concentration if lower_threshold < c < higher_threshold
{ higher_threshold else
A correct non-linear response is yet to be added.
Similarily next steps could include adding a sensitivity decay to ants
as described in the paper.
A proper setup for sane initial values such that interesting behaviour
can be observed can also still be implemented (consult the paper)
The visualization function in server.py can also still be adjusted to
automatically (?) adjust the normalization for color values.
This merge implements server.py for a visualization of the model.
For this implementation the mesa-provided CanvasHexGrid class needed to
be adjusted. Mesa's own function runs a visualization function on all
agents. For us it would be smarter to run the visualization function on
all grid locations, this is what CanvasHexGridMultiAgents does.
For now, concentrations are normalized using magic numbers. In the
future it might be smarter to somehow dynamically adjust these
normalizisation numbers to not threshold our visualization.
Initial implementation of step() accoriding to the rules of Schweitzer
et alii 1996.
A few neighbor fetching functions are now implemented in agent.py.
The MultiHexScalarFields implementation could be simplified as the ants
only drop their pheromones in advance() and not step(), thus they do not
interfere with each other.
We might need to look at the concentration decay for the scalar fields
again to correctly implement it. To me it is not clear whether we should
decrease the pheromone levels before the ants step() or after the ants
step() and before their advance()
