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.
Gradient-based Self-organisation Patterns of Anticipative Adaptation / Sara Montagna; Danilo Pianini; Mirko Viroli. - STAMPA. - (2012), pp. 169-174. (Intervento presentato al convegno 6th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2012) tenutosi a Lyon, France nel 10-13/09/2012) [10.1109/SASO.2012.25].
Gradient-based Self-organisation Patterns of Anticipative Adaptation
MONTAGNA, SARA;PIANINI, DANILO;VIROLI, MIRKO
2012
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.