Graph-based cognitive swarms for object group recognition

A method for graph-based object group recognition incorporating swarming domain classifiers, the method comprising an act of:

• configuring a plurality of software agents to be initialized by a processor such that when initialized, the software agents cooperate as a cooperative swarm to classify an object group in a domain, where each agent's position in a multi-dimensional solution space represents a graph having N-nodes, where each node N represents an object in the group having K object attributes, and where each agent is assigned an initial velocity vector to explore a KN-dimensional solution space for solutions matching the agent's graph such that each agent has positional coordinates as it explores the KN-dimensional solution space, where each agent is configured to perform at least one iteration, the iteration being a search in the solution space for an optimum solution where each agent keeps track of its coordinates in the KN-dimensional solution space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best solution among all agents which corresponds to a best graph among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object group and classify the object group when a classification level exceeds a preset threshold.

A computer program product for graph-based object recognition, the computer program product comprising computer-readable instruction means stored on a computer-readable medium that are executable by a computer for causing the computer to:

• initialize a plurality of software agents to cooperate as a cooperative swarm to classify an object group in a domain, where each agent's position in a multi-dimensional solution space represents a graph having N-nodes, where each node N represents an object in the group having K object attributes, and where each agent is assigned an initial velocity vector to explore a KN-dimensional solution space for solutions matching the agent's graph such that each agent has positional coordinates as it explores the KN-dimensional solution space, where each agent is configured to perform at least one iteration, the iteration being a search in the solution space for an optimum solution where each agent keeps track of its coordinates in the KN-dimensional solution space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best solution among all agents which corresponds to a best graph among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object group and classify the object group when a classification level exceeds a preset threshold.