The 33rd Annual Conference on Innovative Applications of Artificial
Intelligence (IAAI-21) will be virtually held on February 4-6, 2021 along
with the 35th AAAI Conference on Artificial Intelligence (AAAI-21). IAAI-21
is a venue for papers describing highly innovative realizations of AI
technology. The objective of the conference is to showcase successful
applications and novel uses of AI. The conference will use technical
papers, best practice papers, invited talks, and panel discussions to
explore issues, methods, and lessons learned in the development and
deployment of AI applications; and to promote an interchange of ideas
between basic and applied AI and the discourse on the actual deployment of
AI in practice.
IAAI-21 will consider: (1) papers that showcase novel, deployed
applications of AI, and potential applications on this trajectory; (2)
papers that present tools for faster AI solutions development and
deployment; (3) papers that showcase original ways of integrating
methodologies from different areas of AI for practical realization; as well
as (4) best practice papers. Submissions should clearly identify which
track they are intended for, as the tracks are judged on different
criteria. All submissions must be original. *The submission deadline is
September 16, 2020*.
Tracks and Topics
1. Highly Innovative Applications of AI
Papers submitted to this track must describe deployed applications with
measurable benefits that include an innovative use of AI technology.
Applications are defined as deployed once they are in production use by
their final end-users and the in-use experience can be meaningfully
collected and reported. The study may evaluate either a stand-alone
application or a component of a complex system.
Papers will be judged primarily by the quality of: the task or problem
description; the application description; the innovative use of AI
technology; the application use and payoff; and the lessons learned during
application development, deployment and maintenance.
Original papers on the aspects of deploying AI applications in practice are
welcome, and papers, while expected to exhibit both innovative use of AI as
well as demonstrated impact, may focus more on one of these aspects. Each
accepted deployed application paper will receive the IAAI 'Innovative
Application' Award.
Papers in this track may have up to 8 pages in the prescribed AAAI style,
plus at most one more page which may only contain references.
2. Emerging Applications of AI
Emerging applications papers 'bridge the gap' between basic AI research and
case studies of deployed AI applications, by discussing efforts to apply AI
tools, techniques, or methods to real-world problems in novel ways.
Emerging applications focus on aspects of AI applications that are not yet
sufficiently deployed to be submitted as case studies in the first track.
This track is distinguished from reports of purely scientific AI research
appropriate for the AAAI-21 Conference in that the objective of the efforts
reported here should be the potential application of AI technologies,
including engineering considerations. A requirement for papers is to
discuss the path forward for achieving deployment of the technology.
Papers will be judged primarily by the following criteria: significance (of
the problem, and the tool or methodology); relevance of AI technology to
the problem; innovation; path to deployment; content; evaluation; technical
quality; and clarity. Authors are advised to bear these questions in mind
while writing their papers.
Papers in this track may have up to 6 pages in the prescribed AAAI style,
plus at most one more page which may only contain references.
3. Innovative Tools for Enabling AI Application
Within this track, we solicit papers describing tools to improve applied AI
innovation and deployment of AI systems. Areas of interest include, but are
not limited to:
-
Process organization: Tools that help manage and assure the development,
evaluation or deployment of AI systems.
-
Data cleaning: Tools to ease the pain point of processing raw data for
its use in AI systems.
-
Modelling: Tools that facilitate AI modelling, for example approaches to
facilitate deriving models from examples or demonstrations.
-
Meta-algorithmics: Tools to improve AI systems, algorithm configurators,
algorithm portfolios, and hyper-parameter tuners.
-
Platforms: Tools in the form of platforms designed for AI research,
especially those with connections to real world systems and domains.
-
Novel computational models: Tools to exploit new computational hardware,
for example neuromorphic co-processors, quantum computers, and chips
approximating quantum computation.
-
Interaction: Tools that facilitate the interaction design of or user
experience with AI systems, or improve user evaluations.
-
Trusted AI: Tools that generate explanations, justifications, and
persuasions for data-driven models.
Papers will be judged primarily by the following criteria: the extent to
which the tool presented yields solutions with demonstrated improved
quality; lower development, deployment and maintenance costs; better
productivity; fewer errors; and higher ability to scale. Reviewers will pay
attention to how well designed is the tool, how easy is it to use and how
well is it documented, and how many users it has. Note the focus is on the
tool, not on foundational research.
Papers in this track may have up to 6 pages in the prescribed AAAI style,
plus at most one more page which may only contain references.
4. Innovative Inter-disciplinary AI Integration
This track is devoted to the integration of AI components with the focus on
how the orchestration of methods from different AI silos requires the
adaptation of existing technologies to allow them to work together well for
application of AI in practice. Papers must pay attention to engineering
considerations and, where relevant, to human–computer interaction.
Examples for such orchestrated new capabilities and applications include
but are not limited to:
-
Learning search algorithms: Implementations of systematic and heuristic
search algorithms that are capable to improve their performance by learning
from experience or during search.
-
Decision support under uncertainty: Implementations that combine
statistical and deterministic reasoning to provide scalable decision
support under uncertainty for applications that are inherently stochastic.
-
Knowledge representation: Representations that work effectively for
downstream analytics, e.g., the innovative use of a knowledge graph for
feature generation of a downstream machine learning model.
-
Auto-configuration: The adaptation of search methods from combinatorial
optimization to optimize knowledge bases or to design superior forecasting
algorithms.
-
Other research prototypes that integrate algorithms and methods from
traditionally different AI sub-communities.
A clear, transparent, and reproducible case must be presented that allows
the community to judge objectively the value of the innovation of the
integration when compared to existing or ad-hoc approaches. Computational
experiments on benchmarks (that either already are or will be made public)
and in comparison with existing state-of-the-art baseline methods are
expected. At least a portion of the experiments is expected to consider
real-world (not synthetic generated) benchmarks to demonstrate the
practical importance of the problem studied.
Papers in this track may have up to 6 pages in the prescribed AAAI style,
plus at most one more page which may only contain references.
5. AI Best Practices, Challenge Problems, Training AI Users
In this final short paper track, we welcome papers that review best
practices when deploying AI, that communicate novel challenge applications,
and that review contributions that lower the barrier to applying AI by
practitioners outside the AI community. These papers will be reviewed based
on different criteria than the longer papers of the main IAAI tracks. Best
practice papers must tie the recommendations to concrete learning from
prior experience when deploying AI methods. Challenge problem papers must
(this is a hard requirement) make non-generated, real-world benchmarks
publicly available. AI training papers must focus on the particular
challenges when bridging knowledge from application domain experts and AI
expertise and how the domain experts can effectively and efficiently learn
enough about the AI tools they use to apply them successfully. For all
topics in the scope of this track, papers that challenge the status quo are
particularly welcome.
Papers in this track may have up to 4 pages in the prescribed AAAI style,
including references.
Submission Instructions
Electronic submissions are required. Papers must be in trouble-free, high
resolution PDF format and formatted for United States letter (8.5" x 11")
paper. Submissions need to be in AAAI two-column format (Author Kit will be
available soon). Deployed papers can be up to eight (8) pages plus one more
page which may contain only references. Emerging, Tools and Integration
papers can be up to six (6) pages plus one more page which may contain only
references. Best practice, Challenge and Training papers are up to four (4)
pages long only including references. Note that submissions to IAAI-21 may
contain identifying information of the authors and their affiliations:
reviewing is single-blind.
Authors should register on the IAAI-21 EasyChair paper submission site:
https://easychair.org/conferences/?conf=iaai21
Authors must submit a formatted electronic version of their paper through
EasyChair no later than September 16, 2020. We cannot accept papers
submitted by email or fax. Submissions received after the deadline, or that
do not meet the length or formatting requirements detailed above, will not
be accepted for review. Notification of receipt of the electronic paper
will be mailed to the first author (or designated contact author) soon
after receipt. By submitting a paper to IAAI, the authors agree that the
Program Chairs have the final decision on the acceptance of publication at
the conference. Papers will be reviewed by the Program Committee and
notification of acceptance or rejection will be mailed to the contact
author on November 13, 2020. PDFs of accepted papers will be due on
December 18, 2020. Authors will be required to transfer copyright at that
time.
All submissions must be original. IAAI-21 will not consider any paper that,
at the time of submission, is under review for or has already been
published or accepted for publication in a journal or another conference.
Once submitted to IAAI-21, authors may not submit the paper elsewhere
during IAAI's review period. These restrictions apply only to refereed
journals and conferences, not to preliminary versions that are posted as
preprints to arXiv.org or other unrefereed forums, or to workshops with a
limited audience and without archival proceedings. Authors must confirm
that their submissions to IAAI conform to these requirements at the time of
submission.
Organization
Correspondence may be sent to IAAI at iaai21@aaai.org.
IAAI-21 Chairs:
Meinolf Sellmann (GE Research, USA)
Neil Yorke-Smith (Delft University of Technology, Netherlands)
IAAI-21 Outreach Chair:
Thiago Serra (Bucknell University, USA)
--
Thiago Serra, Ph.D.
Assistant Professor of Analytics and Operations Management
Freeman College of Management
Bucknell University
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