The AAAI 2019 Tutorial on Plan, Activity, and Intent Recognition


About the tutorial:

Plan, activity, intent and goal recognition all involve making inferences about other actors (software agents, robots, or humans) from observations of their behavior, i.e., their interaction with the environment and with each other. This synergistic area of research combines techniques from user modeling, machine vision, automated planning, 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, human-robot collaboration and more. This wide-spread diversity of applications and disciplines, while producing a wealth of ideas, models, tools and results, has contributed to fragmentation in the field.

This tutorial seeks to amend this by providing an overview of the recognition literature, as well as a description of the core elements that comprise a recognition problem. In particular, using several motivating examples, we will describe the different recognition tasks, outline their scope, and describe the relationship between them. Finally, we will describe the state of the art of recognition research, highlighting the leading computational representations, algorithms, empirical methodologies, and applications. This half-day tutorial is aimed at general AI students and researches that wish to explore potential research directions in recognition, or use recognition to enhance their ongoing research.

About the presenters:

Sarah Keren (sarah.e.keren@gmail.com), is a post-doctoral fellow at Harvard University. She obtained his Ph.D. from the Technion - Israel Institute of Technology. Sarah's research focuses on redesigning environments for optimized utility. In particular, her Ph.D. established the problem of Goal Recognition Design, dedicated to redesigning goal recognition setting for facilitating online goal recognition.

Reuth Mirsky (dekelr@post.bgu.ac.il), is a Ph.D. candidate at Ben-Gurion University. Her adviser is Dr. Kobi Gal. Reuth’s research focuses on plan recognition in real world environments. In particular, her Ph.D. focused on plan recognition challenges, such as compact problem representation, efficient domain design and hypotheses disambiguation. Her algorithms have been applied in tasks for education, clinical treatment and finance.

Christopher Geib (cgeib@sift.net), is a Principal Researcher at SIFT LLC. He has been the principal architect of multiple probabilistic plan recognition systems including the ELEXIR system that has demonstrated state of the art plan recognition and planning capabilities based on a single shared and learnable representation of the domain.


Particiapation request:

Not only do we want to encourage you to attend, we want you to participate. A significant portion of the tutorial will be a presentation of the state of the art. This will be most compelling if we can present as diverse set of approaches as possible, and we would like your help with this. Established researchers in the area interested in having their work being presented, can provide a small number of slides (in a format we will set) which describe of how their work addresses three (3) recognition domains. More details can be found in the following Google spread sheet