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: personal intelligent assistants, assistive technology in health and smart environments, intelligent human-computer interface, natural language and speech dialogue management, computer and network security, coordination in robots and software agents, and e-commerce and collaborative filtering.

This workshop seeks to bring together researchers and practitioners from these diverse backgrounds, to share in ideas and recent results. In addition to traditional topics in plan, activity and intent recognition and the modeling of other agents, this year workshop will emphasize the discussion of algorithmic challenges and trends in activity and intent recognition and its role in proactive assistive technology technology ranging from smart environments, mobile personal assistance to automated surveillance.

Contributions are sought in the following areas of research:

  • Algorithms for plan, activity, intent, or behavior recognition
  • Machine learning and uncertain reasoning for plan recognition and user modeling
  • Hybrid probabilistic and logical approach to plan and intent recognition
  • Modeling users and intents on the web and in intelligent user interface
  • Modeling users and intents in speech and natural language dialogue
  • High-level activity and event recognition in video
  • Algorithms for intelligent proactive assistance
  • Modeling multiple agents, modeling teams and collaboration teamwork
  • Modeling social interactions and social network analysis
  • Adversarial planning, opponent modeling
  • Intelligent tutoring systems (ITS)
  • Programming by demonstration
  • Cognitive models of intent recognition
  • Inferring emotional states

Related contributions in other fields, are also welcome.


We welcome submissions describing either relevant work or proposals for discussion topics that will be of interest to the workshop. Submissions are accepted in PDF format only, using the AAAI formatting guidelines. Submissions must be no longer than eight pages in length, including references and figures. Please e-mail submissions to cgeib@drexel.edu

Past Workshops



Christopher Geib [Primary contact] (cwg33@drexel.edu) Drexel University
Hung Bui (bui.h.hung@gmail.com) Nuance NLU Lab
David V. Pynadath (pynadath@usc.edu) USC ICT