Neural dynamics for task-oriented grouping of communicating agents

Abstract. Many real world problems are given in the form of multiple measurements comprising local descriptions or tasks. We propose that a dynamical organization of a population of communicating agents into groups oriented towards locally similar clusters of subtasks can identify higher level structure and solve such tasks. We assume that an agent may compute the compatibility of its resources with the input descriptions and that it can compare this compatibility with that of other agents. Based on dynamically updated soft assignment variables each agent computes its action preference distribution and communicates it to other agents. Applying theory developed for the competitive-layer model (CLM, Wersing, Steil, Ritter, Neural Computation 13, 357-387, 2001), a recurrent linear threshold network for feature binding and sensory segmentation, we give constructive conditions on the choice of the agents' compatibility functions and dynamical parameters to assure convergence. They guar...

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Jochen J. Steil
• Keywords: Action Preference Distribution, ESANN 2004, ESANN 2007, Many Real World, Soft Assignment Variables