There is a strong need to develop Artificial Intelligence (AI) for virtual characters which are:

• Autonomous - able to function effectively with little or no human input at runtime

• Reactive - aware of and responsive to the evolving situation and the actions of the trainees

• Nondeterministic - the viewer should never see exactly the same thing twice

• Culturally Authentic - act as a person of the portrayed culture would

• Believable - maintain immersion by acting in a believably human way

This could greatly reduce the training costs, increase accessibility, and improve consistency.

As one aspect of the Future Immersive Training Environment Joint Capabilities Technology Demonstration we created the "Angry Grandmother," a mixed reality character portraying the elderly grandparent of an insurgent whose home is entered and searched by the trainees. She needed to be believable, culturally authentic, nondeterministic, and reactive within the limited scope of the scenario. In addition, she needed to be capable of autonomy, but also responsive to direction from the instructor/operator.

The last 10 years have seen a dramatic improvement in the quality of the AI found in many video games; in our opinion, game AI technology has reached a level of maturity at which it is applicable to immersive training. Accordingly, we built an AI which combines Behavior Trees (BTs) and utility-based approaches. This approach is a descendant of that used in several extremely successful video games, including the Zoo Tycoon 2 franchise, Iron Man, and Red Dead Redemption.

This paper will present the AI architecture which we used for the Angry Grandmother, compare and contrast it to relevant game AI approaches, and discuss its advantages particularly in terms of supporting rapid development of autonomous, reactive characters, but also in terms of enabling that crucial dichotomy between autonomy and operator control.