Today’s robots require a great deal of control and supervision, and are unable to intelligently respond to unanticipated
and novel situations. Interactions between an operator and even a single robot take place exclusively at a very low,
detailed level, in part because no contextual information about a situation is conveyed or utilized to make the interaction
more effective and less time consuming. Moreover, the robot control and sensing systems do not learn from experience
and, therefore, do not become better with time or apply previous knowledge to new situations.
With multi-robot teams, human operators, in addition to managing the low-level details of navigation and sensor
management while operating single robots, are also required to manage inter-robot interactions. To make the most use
of robots in combat environments, it will be necessary to have the capability to assign them new missions (including
providing them context information), and to have them report information about the environment they encounter as they
proceed with their mission.
The Cognitive Patterns Knowledge Generation system (CPKG) has the ability to connect to various knowledge-based
models, multiple sensors, and to a human operator. The CPKG system comprises three major internal components:
Pattern Generation, Perception/Action, and Adaptation, enabling it to create situationally-relevant abstract patterns,
match sensory input to a suitable abstract pattern in a multilayered top-down/bottom-up fashion similar to the
mechanisms used for visual perception in the brain, and generate new abstract patterns. The CPKG allows the operator
to focus on things other than the operation of the robot(s).