This project explores the development of methodologies for populating worlds with persistent, adaptive, collaborative, believable synthetic actors, referred to as Synthespians. These methods are extensions of adaptive models of learning and planning to accommodate the complex, dynamic environments in massive multi-player online games. The intellectual merit includes the development and evaluation of: 1. A behavior development language, with discovery, machine learning, and adaptation of behaviors directly integrated into the language, allowing for the rapid development and deployment of Synthespians. 2. A framework for the actors to recognize and discover plans by observing and modeling the activities of the other agents. An expected outcome of this research is the ability to author complex virtual worlds with many participants that support intelligent and effective interaction between people and machines.

Broader Impact: A scientific understanding of how we interact with each other and collaborate will benefit from our ability to simulate complex environments with dynamic and evolving individual and group behaviors. In this project, building and modeling such environments and behaviors is done within a gaming context. This work will in the long run effect and change the fields of education and entertainment. In addition, being able to model large collaborative and interactive scenarios will also help us understand and model large social dynamics phenomenon of interest to sociologists and economists.

Agency
National Science Foundation (NSF)
Institute
Division of Information and Intelligent Systems (IIS)
Type
Standard Grant (Standard)
Application #
0749181
Program Officer
Ephraim P. Glinert
Project Start
Project End
Budget Start
2007-09-15
Budget End
2008-08-31
Support Year
Fiscal Year
2007
Total Cost
$65,000
Indirect Cost
Name
Georgia Tech Research Corporation
Department
Type
DUNS #
City
Atlanta
State
GA
Country
United States
Zip Code
30332