This AAAI Spring Symposium aims to bring together a multi-disciplinary group of researchers to discuss how we can enable agents to learn from real-time interaction with an everyday human partner, exploring the ways in which machine learning can take advantage of elements of human-like social learning.
The goal of this meeting is to foster a collaborative dialog and bring multiple perspectives to bear on this challenge.  We are seeking broad participation from researchers in:
  1. Machine Learning
  2. Human-Computer Interaction
  3. Human-Robot Interaction
  4. Intelligent User Interfaces
  5. Developmental Psychology
  6. Artificial Intelligence
  7. Adaptive systems
  8. Cognitive Science
  9. Computer Games
  10. Other related fields
We believe that learning will be a key component to the successful application of intelligent agents in everyday human environments (physical and virtual).  It will be impossible to give agents all of the knowledge and skills a priori that they will need to serve useful long term roles in our dynamic world. The ability for everyday users, not experts, to adapt their behavior easily will be key to their success. Machine Learning (ML) techniques have had much success over the years when applied to agents, but ML techniques have not yet been specifically designed for learning from non-expert users and current techniques are generally not suited for it out of the box.
The symposium will cover a variety of topics at the intersection of the various disciplines listed above, for example:
  1.  How do everyday people approach the task of teaching machines?
  2.  What mechanisms of human social learning will machine learning agents need?
  3.  Are there machine learning algorithms that are more/less amenable to learning with non-expert human teachers?
  4.  What are proper evaluation metrics for social machine learning systems?
  5.  What is the state of the art in human teachable systems?
  6.  What are the grand challenges in building agents that learn from humans?
We welcome short and long papers, position statements, videos, and demo proposals as well as panel proposals (indicating the names, affiliations, and email addresses for all panelists).
Please submit your paper of 2-8 pages in PDF AAAI submission format [http://www.aaai.org/Publications/Author/formatting-instructions.pdf] to the Learning From Humans submission site:http://www.easychair.org/conferences?conf=aaaiss09lfh
Submissions will be judged on technical merit and on potential to generate discussion and create community collaboration. The organizers will prepare a technical report summarizing the workshop.  Please direct any submission inquires to aaaiss09lfh@easychair.org.