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(f) feasibility : The “feasible” space (including riding on the backs of other ants) in any direction (m) memory : The memory (implemented as an individual pheromone) of cells in that direction (decay and removal parameters can be specified as well)

(p) pheromone : The pheromone (deposited or persistent) in a particular direction (it is possible to adjust the evaporation and diffusion rate of the pheromone as well)

(a) agent : The agents in a particular direction (s) space : The “available” space in any direction For example, if a stimulus is repulsive everywhere, an influence of “-1” can be used so that each of the gains can be positive. Influence is a shared gain applied to each gain of that stimulus type equally. A negative gain indicates increased stimulus leads to lower chance of choice in that direction. A positive gain indicates increased stimulus leads to a higher chance of choice in that direction. Gain in any particular directly roughly represents a “sensing ability” or a “tendency” how increased stimulus linearly affects whether the agent will decide on that direction. MOVEMENT FACTORS: GAIN, INFLUENCE, AND CONESĮach movement factor “gain” and “influence” variable starts with one or two letters indicating the type of factor. Increasing the bias for any particular direction causes less stimulus to be required for an individual ant to choose that direction.Īmplified stimulus and bias terms are arguments to a logistic function (1/(1+e^(-z))), and so care should be taken when choosing magnitudes of bias and gain terms. The (space) bias terms represent the background tendency for any particular direction of movement. This is an agent-based model that uses logistic switching to balance several different factors that could influence movement in any particular direction.

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Windows and Linux users may obtain the latest Java from

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Mac users must have Mac OS X 10.4 or higher. Java must be enabled in your browser settings. If anyone can help me understand these commands, I would be very grateful.This page was automatically generated by NetLogo 5.0. I already read in the NetLogo dictionary about the commands: 'random' 'random-float' and 'heading' and I couldn't understand why this happens in my code. Can anyone explain to me why this happens? It doesn't happen that the agent doesn't die.

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If I change the set heading random-float 360 line of code from walk 2 to right random-float 360. On walk 2: the problem is that some agents arrive at the edge of the world and don't die and don't do anything else. It doesn't happen that the bee turns around little by little until it finds the inner part of the world and starts moving again. But if I change the line of code right random 360 to right random-float 360. On the walk 1: the problem is that when an agent arrives at the edge of the world (closed world), the agent keeps turning around, little by little, until he turns into the world and starts walking again. A part of the code has two types of walks. I have a world in NetLogo that is closed (box). I have a question about some NetLogo commands.

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