By W. Teahan
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Extra resources for Artificial Intelligence - Exercises - Agent Behaviour I [math]
Try adding obstacles to see how this affects the boid’s ability to cover the entire environment. For example, add obstacles in the form of a maze. Try to create “black spots” where the boid never visits. Alternatively, try to trap the boid into a small area. 38 6. Behaviour Artificial Intelligence: Exercises – Agent Behaviour I EXTENDING THE MODEL The model could be extended to add gradual acceleration and deceleration. This would enhance the boids simulation. NETLOGO FEATURES The code uses the in-cone command to simulate the boid’s cone of vision.
The purpose of this model is to demonstrate some important concepts in relation to communication amongst agents in a network such as the small world phenomenon, degrees of separation, supernodes in peer to peer networks and communication via word-of-mouth. 53 7. Communication Artificial Intelligence: Exercises – Agent Behaviour I HOW IT WORKS The model implements the search algorithms in a novel way by making use of an agent-oriented approach which is relatively easy to implement in NetLogo. The model adopts a purely agentoriented solution where the information is distributed amongst NetLogo agents.
This detracts a little from the believability of the simulation. Is there some way this “bug” can be removed from the model? NETLOGO FEATURES The model uses the command in-radius to implement the boids’ cone of vision. 3. Screenshot of the Interface for the Follow and Avoid model after the setup button has been pressed followed by the go button. 33 6. Behaviour Artificial Intelligence: Exercises – Agent Behaviour I WHAT IS IT? This model attempts to simulate the seeking and fleeing steering behaviours for boids (as devised by Craig Reynolds).
Artificial Intelligence - Exercises - Agent Behaviour I [math] by W. Teahan