By W. Teahan
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Extra resources for Artificial Intelligence - Exercises - Agent Behaviour I [math]
The actual number chosen for a particular node will be a random number between 1 and this number. 57 7. Communication Artificial Intelligence: Exercises – Agent Behaviour I - links-per-super-node: This specifies the maximum number of links a super-node will have. The actual number chosen for a particular super-node will be a random number between 1 and this number. - simulation-ticks: This specifies how long the simulation should run for. - network-update: If non-zero, this will cause the network to be updated at an interval according to a random number from 0 up to the value of this slider.
This model implements a boid (see Craig Reynold's work) that employs basic obstacle avoidance steering behaviour. 36 6. Behaviour Artificial Intelligence: Exercises – Agent Behaviour I HOW IT WORKS It does this by generating a wanderer turtle (boid) that simply wanders around randomly in the environment avoiding the obstacles that are drawn with white patches. The boid is implemented using NetLogo’s in-cone command that implements a turtle with a cone of vision. INTERFACE The model’s Interface buttons are defined as follows: - Setup: This sets up the environment with a tree-like arrangement of obstacles drawn with white patches.
Note also how the next-locations-list parameter for the expand-walkers command determines the knowledge sharing strategy being used that defines the different searches. RELATED MODELS See the Searching for Kevin Bacon model. 1: In the Being Kevin Bacon model, there are many different slider, chooser and switch values to try out when running the search simulation. ) For example, there are five different knowledge sharing methods – “None”, “Word of mouth”, “Blackboard”, “Combined 1” and “Combined 2” – and there are also seven different types of networks – “P2P-nosupernodes”, “P2P-has-super-nodes”, “P2P-random-single-link”, “P2Pincremental”, “P2P-incremental-1”, “Star-central-hub”, and “Hierarchical”.
Artificial Intelligence - Exercises - Agent Behaviour I [math] by W. Teahan