Pheromones, Probabilities, and Multiple Futures

Most agent-based modeling techniques generate only a single trajectory in each run, greatly undersampling the space of possible trajectories. Swarming agents can explore many alternative futures in parallel, particularly when they interact through digital pheromone fields. This paper shows how these fields and other artifacts developed by such a model can be interpreted as conditional probabilities. This interpretation not only allows us to derive more information from them than swarming models usually yield, but also facilitates integrating such models with probability-based AI mechanisms such as HMM’s or Bayesian networks.