Ernest 10.2 simulates spatial movements

Ernest 10.2 anticipates the consequences of his actions in his local map. When the local map predicts that Ernest would bump into a wall, the action of moving forward is inhibited (not enacted) to prevent the bumping.

The local map is now displayed with the shape of a shark in square k6. Unlike in the previous experiment, this display shows the anticipation made during the previous step. For example, on step 16, the local map shows that Ernest was unable to anticipate (from step 15) the appearance of the yellow square in front of him. On step 17, however, he was able to anticipate (from step 16) that the yellow square would shift to the right when he turns to the left (the local map is most often incomplete and sometimes wrong).

When the local map predicts that Ernest would bump into a wall, the local map shows a red circle on Ernest's nose. This occurs for the first times on step 10, 32, and 58. On step 58, the fact that the bumping was prevented is shown by the fact that the wall in front of Ernest does not flash red.

As before, Ernest creates bundles that associate different sensory stimulations together. For example, on step 9, Ernest predicts that moving forward would make him stand on a wall. This false prediction is due to the fact that Ernest does not know yet that walls cause bumping. When Ernest actually experiences the bumping on step 9, he bundles together the tactile stimulation of walls with the bumping stimulation (the dark green color is not associated with this bundle because Ernest's visual attention was distracted by the yellow alga, which caused him to not see the wall). On the contrary, during step 90, Ernest did see the turquoise wall, which caused him to learn the "turquoise wall bundle".

We expect that Ernest's new coupling between his intrinsically-motivated sequential learning system and his spatial representation system can lead to valuable new developments. Yet, many issues remain. In particular, the current implementations of the local map and the bundle mechanism are based on some ad-hoc routines that we have hard-coded. For Ernest to adapt to other environments, these mechanisms need to be learned rather than hard-coded. We believe that such learning could be implemented with statistical methods, such as those used in robotics studies (e.g., Kuipers 2000).

References

Kuipers, B. (2000) The Spatial Semantic Hierarchy. Artificial Intelligence. 119 (1-2), pp 191-233.