Sequence Learning with Intelligent 3-D Practice Environments

Fast Learning against Adaptive Adversarial Opponents

The ability to learn and adapt when playing against an adaptive opponent requires the ability to predict the opponent’s behavior. Capturing any changes in the opponent’s behavior during a sequence of plays is critical to achieve positive outcomes in such an environment. We identify two new requirements that we suggest are essential for agents that learn in adaptive environments. These requireme...

Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data

Recent years have seen a growing interest in intelligent gamebased learning environments featuring virtual agents. A key challenge posed by incorporating virtual agents in game-based learning environments is dynamically determining the dialogue moves they should make in order to best support students’ problem solving. This paper presents a data-driven modeling approach that uses a Wizard-of-Oz ...

Implementing Intelligent 3-D Web-Based-Training Solutions

This presentation describes an approach to implementing web-based-training (WBT) environments using the Virtual Reality Modeling Language (VRML) and Java. A VRML enabled web browser provides an interactive 3-dimensional (3-D) world where the user can learn by discovery and by doing. A Java expert system adds the ability for the practice environment to use reasoning to coach and guide the learni...

Building Intelligent Learning Environments: Bridging Research and Practice

Serious Games Go Informal: A Museum-Centric Perspective on Intelligent Game-Based Learning

Intelligent game-based learning environments show considerable promise for creating effective and engaging learning experiences that are tailored to individuals. To date, much of the research on intelligent game-based learning environments has focused on formal education settings and training. However, intelligent game-based learning environments also offer significant potential for informal ed...