A. J. Gonzalez, W. J. Gerber, R. F. DeMara, and M. Georgiopoulos,
"Context-driven Near-term Intention Recognition,"
Journal of Defense Modeling and Simulation, Vol. 1, No. 3, August, 2004, pp. 122 
- 143.

Abstract:
Recognizing the intention of others in real-time is a critical aspect of many 
human tasks.  This article describes a technique for interpreting the near-term 
intention of an agent performing a task in real-time by inferring the behavioral 
context of the observed agent. Equally significantly, the knowledge used in this 
approach can be captured semi-automatically through observation of an agent 
performing tasks on a simulator in the context to be recognized.  A hierarchical, 
template-based reasoning technique is used as the basis for intention 
recognition, where there is a one-to-one correspondence between templates and 
behavioral contexts or sub-contexts.  In this approach, the total weight 
associated with each template is critical to the correct selection of a template 
that identifies the agent's current intention.  A template's total weight is 
based on the contributions of individual weighted attributes describing the 
agent's state and its surrounding environment.  The investigation described 
develops and implements a novel means of learning these weight assignments by 
observing actual human performance.  It accomplishes this using back-propagation 
neural networks and fuzzy sets.  This permits early discrimination between 
different pre-categorized behavioral contexts/sub-contexts on the human-
controlled agent such as a military or passenger vehicle.  We describe an 
application of this concept and the experimentation to determine the viability 
of this approach.