Modeling Other Agents from Observations (MOO 2004)

Modeling other agents from observations involves learning and reasoning about agents based only on observations of their behavior, i.e., their interaction with the environment and with each other. The observed agents may be synthetic software agents, physical robots, or humans. Given observations of the actions (or other observable features) of agents, a modeling agent's task is to infer their beliefs, plans, intentions, goals, etc.

Workshop Details

• Technical Program (Online proceedings in a single file.)
• Research topics and intended audience.
• Organizing and Program Committees.
• Original Call For Papers (plain text format)

• Plan recognition, behavior recognition
• Adversarial planning, opponent modeling
• Modeling multiple agents, in groups and teams
• User modeling on the web and in intelligent user interfaces
• Acquaintance models
• Modeling other agents in marketplaces and e-commerce
• Agent tracking
• Plan recognition in dialog systems, natural language understanding
• Intelligent tutoring systems (ITS)
• Machine learning for plan recognition and user modeling
• Personal software assistants
• Social network learning and analysis
• Monitoring agent conversations (overhearing)
• Observation-based coordination and collaboration (teamwork)
• Multi-agent plan recognition
• Observation-based failure detection
• Monitoring multi-agent interactions
• Uncertainty reasoning for plan recognition
• Intent inference and Intent recognition
• Commercial applications of user modeling and plan recognition
• Representations for agent modeling
• Modeling social interactions between observed agents
• Applications in security and suspicious behavior recognition
• Inferring emotional states
• Reverse engineering and program recognition
• Programming by demonstration
• Imitation

Intended Audience

Modeling Other Agents (MOO) is a synergistic area of research in artificial intelligence, combining and unifying techniques of plan recognition, user modeling, multi-agent systems, intelligent user interfaces, human/computer interaction, natural language understanding, machine learning, and intention recognition. Agent modeling plays a crucial role in application areas ranging from e-commerce and collaborative filtering, to software assistants, to observation-based coordination, to overhearing, suspicious behavior recognition, and imitation. This wide-spread diversity of applications and disciplines, while producing a wealth of ideas and results, has unfortunately contributed to fragmentation in the field, as researchers publish relevant results in a wide spectrum of journals and conferences.

This workshop seeks to bring together researchers and practitioners of agent modeling from diverse backgrounds, to share in ideas and recent results. It will aim to identify important research directions and to identify opportunities for synthesis and unification.

Workshop Format

The one-day workshop will consist of a series of research presentations, organized into topical sessions (topics to be decided based on submissions). An interdisciplinary panel is planned, seeking to highlight research contributions and challenges unifying and differentiating the different sub-areas.

We are currently examining post-workshop publication venues, such as a journal special-issue and/or edited volume.

Organizers

• Gal A. Kaminka (chair), Bar Ilan University, Israel (galk@cs.biu.ac.il)
• Piotr Gmytrasiewicz, University of Illinois, Chicago (piotr@cs.uic.edu)
• David Pynadath, ISI/University of Southern California (pynadath@isi.edu)
• Mathias Bauer, DFKI, Germany (bauer@dfki.de)

Program Committee

• Dorit Avrahami, Bar Ilan University, Israel
• Tucker Balch, Georgia Tech, USA
• Matias Bauer, DFKI, Germany
• Hung Bui, SRI International, USA
• Cristiano Castelfranchi, Institute of Cognitive Sciences and Technologies, Italy
• Prashant Doshi, Univ of Illinois--Chicago, USA
• Chris Geib, Honeywell Labs, USA
• Piotr Gmytrasiewicz, Univ of Illinois--Chicago, USA
• Marc Huber, Intelligent Reasoning Systems, USA
• Lewis Johnson, University of Southern California, USA
• Meir Kalech, Bar Ilan University, Israel
• Neal Lesh, Mitsubishi Electric Research Lab (MERL), USA
• Itsuki Noda, AIST, Japan
• Michal Pechoucek, Czech Technical University in Prague, Czech Republic
• David Pynadath, University of Southern California, USA
• Zinovi Rabinovich, Hebrew University of Jerusalem, Israel
• Paul Rybski, Carnegie Mellon University, USA
• Eugene Santos, University of Connecticut--Storrs, USA

For additional information