MAML -- Multi-Agent Modeling Language

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MAML Examples

Evolution of Central Places (Urban Morphogenesis)

An agent-based replication of Paul Krugman's Edge City model.

Sorting Ants

A modified version of Eric Bonabeau's model of Cemetery Organization in Ant Societies.
  • Description

  • This is one of the core models of the concept of stigmergy, and in a broader sense, that of decentralized self-organizing systems. It is fairly similar to Deneubourg's Model of Breed Sorting Ants. In this model, agents acting upon local information (from their environment) collect entities (breed or dead bodies), that were initially scattered around the space, into clusters - without any explicit coordination or communication amongst each other. The workl presented here is a slightly modified version of the original model. The interesting phenomenon here is that while the algorithm coded in the individual agents is fairly simple and involves no (direct) communication with the others, the performance of the society results in a fairly ordered landscape. Note that in the original model the ants obtain no information at all from the global level, while in this version they do use the initial density for decision making.

    The algorithm performed by the individual agents is the following: they wander around the space randomly, exhibiting Brownian motion. At each location they decide if swapping the item they are carrying for the one at the given location results in better local organization. (If they are carrying something and there is nothing at the given location, then swapping means putting the carried item down; while when they are carrying nothing but there is some item at the location, swapping means picking the item up.)

  • Source Code for 1.0.2 and for 1.3 (and higher) (antsort.maml)

Traffic Jam

This is a simple simulation vaguely described in Mitchel Resnick's paper, Learning About Life.
  • Description:

  • This model describes cars moving forward in lanes. The cars slow down if they see other cars ahead of them, and stop if they cannot move furhter. On the other hand, if the way clears, they start to move again, increasing their speed until it is possible (as long as there is no car ahead of them, and they have not reached their maximum speed).
    The interesting emerging behaviour to study here is that of the occurrence of traffic jams, which move backward compared to the cars' direction.
  • Source Code for 1.0.2 and for 1.3 (or higher) (tjam.maml)

Dynamical Economy Model of Perazzo, Reich, Schvarzer and Virasoro

This is an agent-based replication of a model, that appeared in Complexity, vol 2, No 6, 1997.


Swarmers' favorite demo application.


Again a popular Swarm demo re-implemented in MAML.
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