1. A processor based method of recognizing a pattern of an object as an object pattern, the method comprising: receiving an activity data set representing an activity of at least one object; generating at least one event from the activity data set; aggregating the at least one event for an area; fusing the event with area information of the area to create a data network; probabilistically recognizing an object pattern from the data network; the object pattern comprises a first object pattern; selecting a second object pattern for a second object based on the first object pattern; responding to the first object pattern with the second object pattern; learning the effect of responding to the first object pattern with the second object pattern utilizing a learning algorithm; and the learning algorithm is a temporal difference learning algorithm comprising a set of observed first object patterns, a set of observed first object reactions, a set of feasible second object patterns, a set of feasible first object reactions, an action-reaction model, and a method for adjusting a weight of at least one of the set of feasible first object reactions based on at least one of the set of observed first object reactions.