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.
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
* How can autonomous learning systems be augmented by explanation systems in
order to improve the human comprehension, transparency, trust, and utility of
* 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
<http://www.aaai.org/Publications/Templates/AuthorKit17.zip%20> (Do not use
earlier versions of aaai.sty). Workshop papers should be submitted to this
* 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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