Needles in a Haystack: Plan Recognition in Large Spatial Domains Involving Multiple Agents

While plan recognition research has been applied to a wide variety of problems, it has largely made identical assumptions about the number of agents participating in the plan, the observability of the plan execution process, and the scale of the domain. We describe a method for plan recognition in a real-world domain involving large numbers of agents performing spatial maneuvers in concert under conditions of limited observability. These assumptions differ radically from those traditionally made in plan recognition and produce a problem which combines aspects of the fields of plan recognition, pattern recognition, and object tracking. We describe our initial solution which borrows and builds upon research from each of these areas, employing a pattern-directed approach to recognize individual movements and generalizing these to produce inferences of large-scale behavior.