Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
EAGER: programming repetitive tasks by example
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Agents that reduce work and information overload
Communications of the ACM
Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Learning to surf: multiagent systems for adaptive web page recommendation
Learning to surf: multiagent systems for adaptive web page recommendation
Cel: a framework for enabling an internet learning community
Cel: a framework for enabling an internet learning community
Multiagent simulation of learning environments
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Educational Data Mining: a Case Study
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Subsymbolic User Modeling in Adaptive Hypermedia
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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We use simulation to evaluate agents derived from humans interacting in a structured on-line environment. The data set was gathered from student users of an adaptive educational assessment. These data illustrate human behavior patterns within the environment, and we employed these data to train agents to emulate these patterns. The goal is to provide a technique for deriving a set of agents from such data, where individual agents emulate particular characteristics of separable groups of human users and the set of agents collectively represents the whole. The work presented here focuses on finding separable groups of human users according to their behavior patterns, and agents are trained to embody the group's behavior. The burden of creating a meaningful training set is shared across a number of users instead of relying on a single user to produce enough data to train an agent. This methodology also effectively smooths out spurious behavior patterns found in individual humans and single performances, resulting in an agent that is a reliable representative of the group's collective behavior. Our demonstrated approach takes data from hundreds of students, learns appropriate groupings of these students and produces agents which we evaluate in a simulated environment. We present details and results of these processes.