NetLogo User Community Models

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## Artificial Life Model

Artificial life model (ALModel) is an ecosystem simulation with autonomous agents (individuals) in a dynamic environment. Like any other artificial life model, it allows exploring real natural phenomena, emergence, and evolution within hours of runtime. It is edifying to study emergent processes through the observation of evolving intelligent agents. Natural selection reveals the main directions of evolution and specialization of various species after several generations of agents in the simulation. This model was also developed for testing of artificial intelligence (AI) of individuals. Currently, two AI models are included: a single large Fuzzy cognitive map (FCM) and an FCM combined with Analytic hierarchy process (AHP).

### How to use it

#### Create world
To start the simulation, you have to create an environment first. Click on 'Create world' on the right (new random seed), or click 'Use and create world' od the left to use seed in the textbox (action key 'R'). You can change settings in the section 'World creation' before the creation (same applies to all settings in the second column on the right - it can be changed only before you generate a world).

If you are not satisfied with a random environment, you can always (even during a model run) use tool 'Edit terrain'. After activation, you click and drag cursor on the map. The height of the patch where you start holding mouse button is stored. Then, you can transfer that height to other patches with cursor (depending on 'tool-range'). For example, to connect 2 islands, start on one of them and draw 'land' to the other. To create an island, start in water and drag cursor around a land you want to isolate.

#### Settings
If you are not sure about settings, use 'Preset scenarios' in the top right corner. Just pick a scenario you want to apply and click on 'Apply settings'. It will change some settings to adjust the simulation in a preferred way. You can combine more scenarios (if they are not contradictory) like 'Big map' and 'Fast evolution' etc. Note that after using a scenario, you will most likely need to recreate a world to apply changes.

#### Individuals
After world creation, you should add individuals into the environment. Before doing this, set number of groups (1 group = about 10 individuals) and the ratio of initial types of species (carnivores, herbivores, omnivores). If you want to watch evolution of a single species in the environment, use switch 'only-1-species'. After some time, it can create a herbivore branch and a carnivore branch; therefore, it may develop a standard predator-prey ecosystem anyway.

To generate groups of individuals, click on 'Create individuals' button in the top left corner (or press action key 'V'). Even during a model run, you can add new individuals. For example, you can observe how ecosystem without predators reacts to new carnivore species in the environment.

#### Before a model run
Before starting a model, don't forget to adjust other settings for your needs. If you want to see actual statistics during the whole run, choose higher 'stats-level'. If you want the model to run fast and see results of evolution, turn statistics down so the model will be faster (in this case, you should turn off 'view updates' in NetLogo and run the version of the model without plots). Feel free to turn off every data collection you don't need.

Also, you can choose population cap in 'Population control' section on the right. This is changeable during a model run, so you can always adjust desired simulation size/speed ratio.

#### Starting a model run
When environment and individuals are generated, you can start simulation by clicking on 'Run' (or pressing key 'W'). When the model is running, you can still change some settings in the first column on the right, but not all changes apply immediately, effects shows with a delay and something applies only to newly created individuals.

#### Stopping a model run
The model will run forever until user stops it or the number of individuals drops to zero. Output of the model can be (depending on 'stats-level' setting) the final most successful behavior model settings, (un)balance in the environment, the most successful attributes, observation of an emergence or behavior of a single individual, phylogenetic trees of species (use Phylogenetic Tree Viewer), etc.

### Author

Tomas Nachazel (2015)
tomas.nachazel@uhk.cz

Faculty of Informatics and Management
University of Hradec Králové
Czech Republic