Intelligent pedagogical agents with multiparty interaction support

Abstract

Virtual learning worlds with embodied pedagogical agents can provide an effective environment for experientially grounded learning. However, such learning environments to date have been confined to one agent and one user. While a single agent single user setting simplifies interaction modeling, the richness of naturalistic multiparty interaction is severely compromised. In addition, the potential benefits of collaborative learning cannot be realized.In this thesis, we analyze the different capabilities that agents need to possess to behave believably in the context of multiple users and multiple agents. A generic four-layer agent architecture with multiparty interaction support is introduced to address the challenges that arise in agent planning and task execution, communication and understanding, as well as effective coaching of student learning. A Newtonian 3D learning environment for agents and users is presented to illustrate the effectiveness of the agent architecture. An evaluation was conducted to determine the naturalism of the multiparty interaction and the extent of improvement in student learning.The approach we have adopted in constructing agents with multiparty interaction support can be regarded as a generic step towards addressing and solving issues related to effective student interaction and learning for a 3D virtual learning environment in any sophisticated domain of learning.