Plan recognition, activity recognition, and intent recognition all involve making inferences about other actors from observations of their behavior, i.e., their interaction with the environment and with each other. The observed actors may be software agents, robots, or humans. This synergistic area of research combines and unifies techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including:
- Assistive technology
- Software assistants
- Computer and network security
- Behavior recognition
- Coordination in robots and software agents
- E-commerce and collaborative filtering
This wide-spread diversity of applications and disciplines, while producing a wealth of ideas and results, has contributed to fragmentation in the field, as researchers publish relevant results in a wide spectrum of journals and conferences. As there is no commonly accepted conference for this work, this workshop provides a valuable place to discuss, standardize and improve past work of this sub-field. Thus, this workshop seeks to bring together researchers and practitioners from diverse backgrounds, to share ideas and recent results. It aims to identify important research directions, opportunities for synthesis and unification of representations and algorithms for recognition. Contributions of research results are sought in the following areas of:
- Plan, activity, intent, or behavior recognition
- Adversarial planning, opponent modeling
- Modeling multiple agents, modeling teams
- User modeling on the web and in intelligent user interfaces
- Plan recognition and user modeling in marketplaces and e-commerce
- 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
- 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
- Plan recognition in hierarchical planning
Due to the diversity of disciplines engaging in this area, related contributions in other fields, are also welcome.
Submission Guidelines:
All submissions must be original. If a work is under submission for the main conference as well or for a different conference, it should be written in the title. Papers must be in trouble-free, high-resolution PDF format, formatted for US Letter (8.5" x 11") paper, using Type 1 or TrueType fonts. Submissions are anonymous, and must conform to the AAAI-23 instructions for double-blind review.
While all submissions will be peer reviewed to determine acceptance, note that this is a non-archival workshop. We will accept submissions of papers under review to other venues. However, if your paper is accepted to an earlier conference or ICAPS, we expect you to withdraw your submission to PAIR.
Full Papers:
We accept full paper submissions. Papers must be formatted in AAAI two-column, camera-ready style; see the AAAI 2023 author kit for details:
Submissions may have up to 8 pages including references.
Confirmed Keynote Speakers:
TBA
Cochairs:
[Primary contact] Richard Magnotti, Rutgers University (richard.magnotti@rutgers.edu), [Primary contact] Simona Ondrckova, Charles University (ondrckova@ktiml.mff.cuni.cz), Denson George, Rutgers University (dg1013@cs.rutgers.edu), Kristyna Pantuckova, Charles University (pantuckova@ktiml.mff.cuni.cz), Christopher Geib, SIFT (cgeib@sift.net)