This article describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality-based training system. This project is an ambitious exercise in integration, both in the sense of integrating technology with entertainment industry content, but also in that we have joined a number of component technologies that have not been integrated before. This integration has not only raised new research issues, but it has also suggested some new approaches to difficult problems. We describe the key capabilities of the virtual humans, including task representation and reasoning, natural language dialogue, and emotion reasoning, and show how these capabilities are integrated to provide more human-level intelligence than would otherwise be possible.
Aliod, Diego Molla, Alonso, Eduardo, Bangalore, Srinivas, Beck, Joseph, Bhanu, Bir, Blythe, Jim, Boddy, Mark, Cesta, Amedeo, Grobelink, Marko, Hakkani-Tur, Dilek, Harabagiu, Sanda, Lege, Alain, McGuinness, Deborah L., Marsella, Stacy, Milic-Frayling, Natasha, Mladenic, Dunja, Oblinger, Dan, Rybski, Paul, Shvaiko, Pavel, Smith, Stephen, Srivastava, Biplav, Tejada, Sheila, Vilhjalmsson, Hannes, Thorisson, Kristinn, Tur, Gokhan, Vicedo, Jose Luis, Wache, Holger
The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.
With the growing importance of multiagent team-work, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. ISAAC'S novelty stems from a key design constraint that arises in team analysis: Multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. Additionally, ISAAC uses multiple presentation techniques that can aid human understanding of the analyses. This article presents ISAAC'S general conceptual framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup Scientific Challenge Award.