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Implemented check for ants to follow positive pheromone gradient

step() function has been improved.
Previously, self.prev_pos (now: self._prev_pos) was set non-uniformly;
sometimes in step() but also always in advance() which resulted in
unwanted behavior.
As self._prev_pos is now marked as private all assignments are done
simultaneously with self._next_pos in step(), not advance().

Accordingly, future functions should strive not to access
self._prev_pos outside of the agent if possible.

Gradient following behaviour was additionally not observed since the
sensitivity_max variable was set too low, resulting in the adjusted
pheromone concentration gradients to not be present.

2023-05-18 16:02:13 +02:00

agent.py

implemented test ant_follow_gradient, fixed step() function

2023-05-18 16:00:43 +02:00

LICENSE

Initial commit

2023-04-26 21:07:21 +02:00

main.py

implemented test ant_follow_gradient, fixed step() function

2023-05-18 16:00:43 +02:00

model.py

implemented test ant_follow_gradient, fixed step() function

2023-05-18 16:00:43 +02:00

multihex.py

fix Grace's name

2023-05-17 15:57:23 +02:00

README.md

add server visualization to README

2023-04-29 11:02:55 +02:00

server.py

implemented test ant_follow_gradient, fixed step() function

2023-05-18 16:00:43 +02:00

shortlist.md

add initial project files

2023-04-26 23:45:14 +02:00

README.md

ants

A reimplementation of Schweitzer et al. 1996 as well as additional improvemnts for the Course Agent Based Modelling for Social Systems FS2023 ETH Zürich

For the course Agent Based Modelling for Social Systems FS2023 we were tasked to implement a model of our own (in groups). For this, we decided to implement an enhanced version of Active random walkers simulate trunk trail formation by ants (Schweitzer et al. 1996) using Python and Mesa.

For now, wanted features can be found in our shortlist. For everything else start at main py

For a live visualization of the project you can execute server.py.