The self-organisation Gradient pattern is known to be a key spatial data structure to make information local to its source become global knowledge, and to dynamically and adaptively steer agents to that source even in mobile and faulty environments – e.g. when obstacles unpredictably appear. In this paper we conceive new self-organisation mechanisms built upon this pattern to tackle anticipative adaptation. We ensure that the retrieval of a target of interest proactively reacts to locally-available information about future events, namely, the knowledge about future obstacles (e.g., expected jams or road interruption in a traffic control scenario) is used to emergently compute alternative and faster paths.
