Development of swarm behavior in artificial learning agents that adapt to different foraging environments
Fig 6
Learned behavior at the end of the training process.
The final probabilities in the agents’ ECM for the action “go” are shown for each of the 25 percepts (5x5 table). (a) and (b) Final probabilities learned in the scenarios with dF = 21 and dF = 4 respectively. The average is taken over 20 ensembles (each learning task) of 60 agents each. Background colors are given to easily identify the learned behavior, where blue denotes that the preferred action for that percept is “go” and orange denotes that it is “turn”. More specifically, the darker the color is, the higher the probability for that action, ranging from grey (p ≃ 0.5), light (0.5 < p < 0.7) and normal (0.7 ≤ p < 0.9) to dark (p ≥ 0.9). Figures (c) and (d) show what the tables would look like if the behavior is purely based on alignment (agent aligns to its neighbors with probability 1) or cohesion (agent goes towards the region with higher density of neighbors with probability 1), respectively. See text for details.