Information Technology Reference
In-Depth Information
process does not interfere with speech processing or 3D scene rendering, both of
which require significant amounts of computing resources.
Our most common approach for specifying agent intent planning is through finite
state machines. Our dialog authoring tools allow authors to specify the phases of each
dialog, the communicative acts the learner might perform at each phase, and the acts
that the agent should choose in response. This approach is highly authorable and has
been used to create hundreds of dialog models for a variety of different cultures and
agents. Unfortunately, it is somewhat lacking in versatility; agent models must be
authored specifically for each agent in each scenario. To address this limitation, VRP
provides a rule-based intent-planning engine that enables authors to specify cultural
behavior rules. Each rule specifies a learner communicative act that the agent should
respond to, a set of conditions that must be met, and the effects of applying the rule.
Conditions can refer to properties of the learner's communicative act (e.g., its degree
of politeness), the state of objects in the virtual world, and properties of the social
relationships between the characters in the scenario. For example, the response of a
non-player character such as the village elder in Fig. 1 depends upon the extent to
which the learner has developed the village elder's trust, which in turn depends upon
what the learner has said and done up to that point.
Once each agent in the conversation chooses an action to perform, the sociocultural
simulation module generates behavior to realize the action. This typically includes
selecting an utterance that realizes the utterance in the target language, as well as one
or more animated gestures. These in turn are passed to the game engine platform for
execution.
Using the agent behavior authoring tools, we create libraries of reusable agent
models. Authors can then create cultural training scenarios by taking a virtual world
representing the cultural environment, populating it with non-player characters, and
assigning agent models to each character appropriate for that character's role in the
scenario. Example roles include village elders, shopkeepers, and passers-by on the
street. Each agent will respond to the learners in a manner appropriate for the culture,
the agent's role in the scenario, and the current state of the unfolding situation.
Authors can extend and modify the rule sets of the agents if needed to reflect the
specific characteristics of the scenario.
6 Evidence of Effectiveness
The effectiveness of this approach has been documented in various published studies
(e.g., [12]). The most dramatic evidence of effectiveness comes from a study
conducted by the US Marine Corps Center for Lessons Learned (MCCLL) [7], which
studied the experience of the 3
rd
Battalion, 7
th
Marines (3/7 Marines) in Anbar
Province, Iraq in 2007. Prior to deployment to Iraq, the battalion assigned two
members of each squad of approximately thirteen marines to spend forty hours in self-
study training with Alelo's Tactical Iraqi course. It should be noted that (1) forty
hours is not a long time to spend learning a foreign language, (2) Arabic is a difficult
language for most English speakers, and (3) self-study computer-based language
learning tools often fail to produce significant learning gains. However the 3/7's
experience drew attention both prior to deployment and after deployment. In final
