Fellow Researchers,

We would like to invite you to submit to and participate in HMCL 2017 - The 1st 
AAAI Workshop on Human-Machine Collaborative Learning, which will be co-located 
with AAAI, in San Francisco, CA, USA on February 4th, 2017. Please refer to the 
website for further information: https://blogs.parc.com/aaai-17/.  Please note 
that the submission deadline has been extended to Friday, November 4th, 2016. 
Please forward to fellow researchers you think may be interested in the topic.

Hoda Eldardiry,
HMCL Cochair


Early work in artificial intelligence and expert systems demonstrated the 
potential of high performing systems in complex domains, but required extensive 
and often impractical amounts of knowledge engineering to achieve that 
performance. In the past few years, work in deep learning methods have provided 
another path to high performance, but required vast numbers of training 
examples that were impractical to collect or develop for most applications. 
This has triggered exploration across several disciplines ("cogno-social") to 
understand leverage points that exploit combinations of lightweight models with 
learning, sub-symbolic (model-free) and symbolic computational approaches, 
qualitative modeling, and human-computer interaction approaches that connect 
human knowledge or feedback with machine learning.
The first AAAI Workshop on Human-Machine Collaborative Learning (AAAI HMCL 
2017) will focus on human-machine collaborative learning, an interdisciplinary 
research direction that integrates machine learning, cognitive modeling, 
human-computer interaction and social dimensions of learning. The goal of this 
workshop is to encourage and solicit research in the area of human-machine 
collaborative learning, i.e., development of systems and approaches that enable 
collaborative ensembles of people and computers to learn and adapt more 
rapidly, reliably and profoundly.


We are looking forward to submissions focusing on the following questions, 
challenges and opportunities:
* What learning factors limit creation, combination and use of knowledge in 
human groups?
* How can autonomous learning systems be augmented by explanation systems in 
order to improve the human comprehension, transparency, trust, and utility of 
machine learning?
* How can human capabilities of creating common ground in communication be 
extended to computer systems in order to foster mutual understanding and 
collaboration and teaming in tasks requiring discovery and new abstractions?
* How can human knowledge and experience in "open worlds" be combined with 
tireless and systematic work of computers to greatly increase the rate of 
effective and robust learning in human-machine ensembles?
* How can use of multiple existing bodies of knowledge, use of analogy, 
multiple predictive models and reflection be combined with machine learning 
methods to direct learning in productive directions without catastrophically 
limiting the system's capacity for original thinking and learning.
* How can machines learn from context in an environment to guide autonomous 
How do computational considerations limit the ultimate generality, speed and 
utility of learning by teams of machines and people?


* Workshop Date: February 4th, 2017
* Workshop Schedule: TBD


* October 21: Submissions due (unless noted otherwise)
* November 18: Notification of acceptance
* December 8: Camera-ready copy due to AAAI
* February 4: Workshop

* HMCL17 accepts regular research papers no longer than 8 pages and short 
papers (4 pages) on preliminary works presenting position ideas for new 
* Submission URL: TBD.
* Submission Deadline: October 21st, 2016.
* Submission Format: AAAI two-column format is often required for workshop 
submissions, and will be required for all final accepted submissions. The AAAI 
Press author kit with style files, macros, and guidelines for this format is 
located atwww.aaai.org/Publications/Templates/AuthorKit17.zip 
<http://www.aaai.org/Publications/Templates/AuthorKit17.zip%20> (Do not use 
earlier versions of aaai.sty). Workshop papers should be submitted to this 
link:  https://easychair.org/conferences/?conf=hmcl2017.

* Hoda Eldardiry (Palo Alto Research Center - PARC)
* Ken Forbus (Northwestern University)
* Christian Lebiere (CMU)
* Kumar Sricharan (Palo Alto Research Center - PARC)
* Mark Stefik (Palo Alto Research Center - PARC)

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