Modeling Others from Observations (MOO 2006)

Modeling other actors from observations involves learning and reasoning about actors based only on observations of their behavior, i.e., their interaction with the environment and with each other. The observed actors may be software, robots, or humans. Given observations of the actions (or other observable features) of actors, a modeling system's task is to infer their beliefs, plans, intentions, goals, social commitments, roles, etc. This is a synergistic area of research combining and unifying techniques of plan recognition, intent recognition, user modeling, intelligent user interfaces, human/computer interaction, autonomous and multi agent systems, natural language understanding, and machine learning. Such modeling plays a crucial role in a wide variety of applications including:

• e-commerce and collaborative filtering
• software assistants for work-related tasks, assistive living, etc.
• observation-based coordination in robots and software agents
• computer and physical security
• suspicious and anomalous behavior recognition

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 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. This workshop seeks to bring together researchers and practitioners 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. The workshop will especially emphasize a discussion of challenges and trends: Reasoning with incomplete or incorrect knowledge of others, agent modeling on the web and in open multi-agent systems, modeling multiple agents and the interactions between them, inferring the capabilities of agents from observations, and using agent modeling for coordination and teamwork. Contributions are sought in the following areas of research:

• Plan recognition, behavior recognition
• Adversarial planning, opponent modeling
• Modeling multiple agents, modeling teams
• Relevant techniques from User Modeling and intelligent user interfaces
• Acquaintance models
• Agent modeling in marketplaces and e-commerce
• Student modeling in 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
• Inferring emotional states
• Reverse engineering and program recognition
• Programming by demonstration
• Imitation

Due to the diversity of disciplines engaging in modeling agents based on observations, relevant contributions in other fields are also welcome.