This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Gonzalez, A. J. Articles by Georgiopoulos, M. Search for Related Content Social Bookmarking                         What's this?

Context-driven Near-term Intention Recognition

Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816-2450

William J. Gerber

Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816-2450

Ronald F. DeMara

Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816-2450

Michael Georgiopoulos

Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816-2450

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 significant, 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.

Key Words: DIS • network bandwidth

The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, Vol. 1, No. 3, 153-170 (2004)
DOI: 10.1177/875647930400100303


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?