The basic concept of such an ACO world, is to take some cost surface and transform it to a penalty layer. Even if the algorithm comes from the realm of insects, it might be adopted to different animal kingdoms. Depending on the type of agent this penalty layer must be reinterpreted: if for example we want to talk about human agents the penalty layer may be expressed by the walking velocity, e.g. calculated with the algorithm proposed by Tobler1993. The actors on the playground will wander around on the playground using the time for their paths that correspond with the values in the penalty grid. If they find some attractor, they walk home to the position they originated, marking their way with pheromones. While this pheromone vanishes over time, the following agents are more likely to choose their next steps to a position that smells most.
This first toolset was mainly developed for Topoi Project A-III-4, with some inspirations from previous work conducted at the Uni Bern in 2008.
ACO works best on dynamic maps -- it constantly tries to improve paths...
r.agent.aco outputmap=out.map penaltymap=testpenalty.grid \ sitesmap=sites.vect rounds=100 outrounds=100 volatilizationtime=5000 \ antslife=2000 maxants=400 pathintensity=1000000
For running the total test suite on the libraries, i.e. to run all the tests that are at the end of each python file, use this test collection (for certain tests, the following files must exist though: "elev.grid", and "arch.vect"):
user@host:~$ cd /<pathtoaddons>/r.agent/libagent user@host:libold$ ./alltests.py
Improve encapsulation of classes.
Find good parameters, or parameter finding strategies for the ACO part. Try to avoid high penalty fields. Think about heuristics too.
Implement other ABM scenarios.
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