Emulation of emotion using vision with learning

Concerns the implementation of emotional behavior in an artificial agent. Action Selection Dynamics (ASD), or Behavior Network, provides Subsumption Architecture (SA) with additional flexibility in coordinating behavior activation in a fully distributed manner. The agent can effectively monitor the environment and adapt to changes as they occur rather than the human observer having to manually adjust the priority structure that governs behavior action. The addition of vision to this framework provides an additional sensory input so that the SA robot with ASD is further aware of its environment at all times. The vision system was designed and implemented based on a behavior-based vision methodology. With the vision added, the robot can respond to minute changes in the environment, which other sensors are not capable of detecting, while at the same time it enhances the area of the environment that is monitored. This makes the interaction between the robot and human considerably richer. Responses by the robot are based on comments the robot receives from a human interrogator. The robot´s response to its environment is unique each time it is run, an important consideration when emulating human-like emotions. Further interaction with humans in the environment is provided with the addition of speech recognition/synthesis capabilities to the robot. Furthermore, the robot will not only demonstrate human-like emotions through its behaviors but with the addition of the speech it can also vocalize those emotions