Summaries of Workshops held at IJCAI 2016 at New York in July

Summaries
of
Workshops*

Held
at
IJCAI
2016,
NY
Workshop
track
organized
by:

Biplav
Srivastava,
IBM
Research
&

Gita
Sukthankar,
University
of
Central
Florida
July
2016
IJCAI
2016
@ijcai16
*
Subset
which
agreed
to
make
slides
public.
Workshop
list
is
at:
http://ijcai-‐16.org/index.php/welcome/view/accepted_workshops

<W2>
IJCAI
2016
Workshop
on

“Scholarly
Big
Data:
AI
Perspectives,
Challenges,
and
Ideas”
www.cse.unt.edu/~ccaragea/ijcai2016
ws.html
• Workshop
Highlights
• The
primary
goals
and
objectives
of
the
workshop
are
to
promote
both

theoretical
results
and
practical
applications
for
scholarly
big
data,
and

address
challenges
that
are
faced
by
today’s
researchers,
decision
makers

and
funding
agencies
as
well
as
well-‐known
technological
companies
such
as

Microsoft
and
Google.
• Results
from
the
workshop:
• Two
invited
talks:
“Microsoft
Academic
Service:

Challenges
and
Opportunities”
by
Iris
Shen;
and

“Introduction
to
Scholarly
Big
Data”
by
Lee
Giles
• Several
paper
presentations
on
topics
as
diverse
as:

Inventor
Name
Disambiguation;
Identifying
Near-‐
Duplicated
Literature
in
CiteSeerX;
Computer
Science

Paper
Classification;
and
Identifying
Promising
Research

Directions.

Motivation
• Massive
amounts
of
scholarly
documents

including
papers,
books,
technical
reports,

etc.
and
associated
data
such
as
tutorials,

proposals,
and
course
materials

• There
is
a
high
need
for
automated
tools
for

mining,
managing
and
searching
scholarly

big
data
(SBD)
Conclusion
• The
workshop
not
only
brought
together

researchers
working
SBD,
but
also
served
as

a
venue
for
informing
researchers
about
this

rapidly
growing
and
remarkably
important

domain.

W04
IJCAI
2016
Workshop

on
Goal
Reasoning
http://makro.ink/ijcai2016grw
Workshop
Highlights
• Invited
talk:
David
Aha
(NRL)
reviewed
previous
three
workshops,

highlighted
underexplored
avenues
of
investigation.
• Invited
talk:
Sebastian
Sardina (RMIT)
reviewed
Goal
Reasoning
in

BDI
systems,
highlighted
opportunities
for
further
collaboration.
• Assumption
of
static,
user-‐provided
goals
challenged.
• New
formal
models
of
goal
reasoning
mechanism
&
representations.
• Relationships
to
MDPs
and
automated
planning
explored.
• Modeling
design
process
as
iteratively
operationalizing
ill-‐defined

goals
with
curiosity
constraint.
• Violation
of
expected
states
appear
to
be
a
common
trigger
for

initiating
goal
reasoning.
• Goal
recognition
used
to

reason
about
other
agents’
goals.
• Goal
reasoning
algorithm
control

for
$100K
UUV
test
fielded.
• Select
papers
to
be
published

in
AI
Communications.

Motivation
Goal
structures
can
help
manage
long-‐term

behavior,
anticipate
the
future,
select
among

priorities,
and
adapt
to
surprise.

Conclusion
New
insights:
• A
strong
affinity
with
BDI
systems
exists
New
directions
include:
• Problem
recognition
&
formulation
• Focus
of
attention
models
• User
interaction
&
Human/System
Teams
• Embedding
social
norms
• Graceful
degradation
• Reproducibility
of
studies
• Learning
useful
goal
states
Control
architecture
for
UUV
with

Goal
Reasoning
(Wilson
et
al.
2016)

<W05>
2nd IJCAI
2016
Workshop
on
Social
Influence
Analysis
Site:
http://socinf2016.isistan.unicen.edu.ar/
Workshop
Highlights
•Four technical papers
• Diverse social networks such as Twitter and Pinterest,
hypergraphs and even small groups (business meetings,
group discussion).
•Alibaba Tianchi Alibaba “Brick-‐and-‐Mortar Store
Recommendation with Budget Constraints”
• 10k USD in prizes.
•Two Invited talks
• Big Network Analysis—Algorithms, and Applications (by
Jie Tang).
• Negative Social Influence in Online Discussions (by
Justin Cheng).
Motivation
•Influencers have high impact on the opinions and
behaviorsof other users.
•The discovery of influencers is a complex problem
that requires developing models, techniques
and
algorithms for an appropriate analysis of the
currentsocial network.
Conclusion
•Research gaps in the field were identified.
•Interesting discussions were generated about
possible approaches to social influence
analysis.

W06
IJCAI
2016
Workshop
on

Ethics
for
Artificial
Intelligence
Site:<https://www.cs.ox.ac.uk/efai>

• Workshop
Highlights
• There
was
lively
discussion
of
different
approaches
to
understanding

the
future
potential
of
AI
for
good
and
its
potential
dangers
• Topics
ranged
from
the
immediate
problems
facing
AI
right
now,
such

as
problems
regulating
autonomous
vehicles
and
issues
of
liability
• -‐ to
discussions
of
how
humankind
might
relate
to
superintelligent AI
• Papers
included
both
theoretical
and
speculative
accounts,
as
well
as

lab-‐based
experiments
on
the
nature
of
robot
transparency
• This
is
helpful
for
appreciating
the
diversity
of
approaches
to
these

issues,
drawing
on
empirical
lab
work,
work
on
differing
legal

approaches
in
various
jurisdictions,
and
work
gaining
inspiration

from
philosophical
approaches
to
the
nature
of
our
ethical
life
• As
well
as
a
wide
divergence
of
views,
there
seems
to
be
progress
in

addressing
ethics
in
AI,
with
greater
understanding
and
clarity
among

the
audience
of
what
the
issues
are
and
promising
ways
to
tackle

them
Motivation
• There
is
increasing
awareness
of
the
need

to
examine
the
ethical
challenges
of
AI.

• These
include
not
just
potential
dangers

of
the
use
of
various
forms
of
AI
but
ways

to
maximize
the
potential
benefits
of
AI
Conclusion
• There
is
a
great
diversity
of
views
and

strong
opinions
on
this
topic!
• From
constructive
discussions
such
as
this

we
can
move
forward
the
field,
help
gain

public
trust
and
provide
beneficial
AI
for

the
future

W7
IJCAI
2016
Workshop
on

Computational
Models
of

Natural
Argument
Workshop
Highlights
• 6
papers,
2
research
abstracts,
and
a
keynote
talk
• Topics
of
presentations:

• Argument
mining
in
biomedical
publications
• Argumentative
devices
in
healthcare
publications
• Representing
rhetorical
figures
for
argument
mining
• Representing
arguments
in
social
media
• Multi-‐disciplinary
analysis
of
political
argumentation
• Argumentation
tools
for
intelligence
analysts
• Computational
argumentation
and
decision
making
Motivation
In
the
16th year
of
this
workshop
series,

CMNA
16
serves
the
community
working
on

Argument
and
Computation,
a
field

developed
in
recent
years
overlapping

Argumentation
Theory
and
AI.
The
workshop

focuses
on
modeling
"natural“

argumentation,
where
naturalness may

include
expression
in
text,
multimedia
,
or

graphics,

use
of
rhetorical
devices,
and/or

taking
into
account
characteristics
of
the

audience
such
as
affect.
Conclusion
• Schemes

And
other
logic+/-‐ representations
• Data
Argument
mining
Mining
arguments
• Social
media
as
source
and
destination.
http://cmna.info/CMNA16/

W8 Interactive
Machine Learning:
Connecting Humans and Machines
Site:sites.google.com/site/ijcai2016iml
• Workshop
Highlights
• Invited
talks:
• Peter
Stone
(UT
Austin)
• Michael
Littman
(Brown)
• Brenden
Lake
(NYU)
• Maya
Cakmak
(UW)
• Lively
panel
discussion
• Teaching
intelligent
agents
using
stories
• Using
a
curriculum
to
teach
increasingly
complex
tasks
• Asking
the
“right” questions
is
key
• Multiple
information
sources,
transparency
to
user
• Applications:
robotics,
topic
models,
maintenance
costs
• Website
accessed
~2500
times,
industry
interest
Motivation
• ML
as
a
continuous
process
• Human
interaction
– Dialog
• Small
data
vs.
Big
data
• Which
Representations?
• Which
Algorithms?

• Which
Interfaces?
Conclusion
• Rethink
basic
tenets
• Human
≠
reward
function
• Difficult
intersection
of
fields

• Better
integration
with
cognitive
science,

HCI
community
Organizers: Kaushik
Subramanian,
Heni
Ben
Amor,

Andrea
Thomaz,
Charles
Isbell

The
10th
Multidisciplinary
Workshop
on

Advances
in
Preference
Handling
(M-‐PREF)

Workshop
Highlights
• Invited
talk
by
Vincent
Conitzer
on
“Mechanism
Design
in
Data-‐Rich

Environments”
• Justified
representation
&
iterative
voting
with
deadlines
• Domain
restrictions
for
votes
with
ties
• Winner
determination
for
large
instances
with
MapReduce
• Computing
norm
support
in
virtual
communities
• Preference
elicitation
for
scheduling
devices
in
smart
buildings
• Preference
networks:
constrained
versions
and
efficient
satisfiability

checking
• A
probabilistic
graphical
model
for
Mallows
preferences
• Moral
preferences
Motivation
lPreferences
are
a
central
concept
of
decision
making
and

used
in
fields
including
AI,
databases,
and
human-‐computer

interaction
lThis
workshop
brings
together
researchers
from
numerous

sub-‐fields,
who
are
interested
in
computational
aspects
of

preference
handling

lAim: Report
on
novel
and
emerging
research
on
preferences

and
provide
an
opportunity
for
cross-‐fertilization
between

fields
Conclusion
lNoteworthy
progress
in
established
areas
including
voting,

databases,
and
knowledge
representation
and
reasoning
lNew
research
challenges
such
as
big
data
and
integrating

morality
http://www.mpref-‐2016.preflib.org/
W9
@
IJCAI
2016

<W10>
IJCAI
2016
Workshop

on
Biomedical
infOrmatics

with
Optimization
and

Machine
learning
(BOOM)
Site:
http://www.ijcai-‐boom.org
Workshop
Highlights
v Full Paper Track: 12 submissions. 5 with the finest first-‐round reviews invited
for oral presentation. Expected to finally accept 6-‐7 for the special issue.
v Short Abstract Track: 13 submissions. 10 accepted for spotlight/poster
presentation.
v 5 Invited Plenary Speakers + Panel Discussion.
v Best Paper Awards sponsored by Microsoft Research.
v More than 40 people attended this full-‐day workshop.
Conclusion
• The
BOOM
workshop
catalyzed synergies
among
biomedical
informatics,

machine
learning,
and
optimization.
• It fosters exchange
of
ideas
between
often-‐disparate
groups
that
are
unaware

of
each
other's
research,
and
to
stimulate
fruitful
collaborations
among

different
disciplines.

• Biomedical
data
often
feature
large
volumes,
high
dimensions,
imbalance

between
classes,
heterogeneous
sources,
noises,
incompleteness,
and
rich

contexts.
Such
demanding
features
are
also
driving
the
development of novel
machine
learning and optimization
algorithms.
Motivation
• A compelling demand for novel machine learning, data
mining and optimization algorithms to specifically tackle
the unique challenges associated with biomedical and
healthcare data.
• Recent major breakthroughs in machine learning that is
equipped with powerful optimization technologies
(deep learning,etc.)
• Idea exchanges among applied mathematicians,
computer scientists, bioinformaticians, computational
biologists,industrial engineers,clinicians and healthcare
researchers.
See You At Next BOOM!

W12
IJCAI
2016
Workshop
on

Language
Sense
on
Computers
Organizers:
Akinori Abe
&
Rafal
Rzepkahttp://ultimavi.arc.net.my/ave/IJCAI2016/
• Workshop
Highlights
• Many
rare
and
novel
findings
were
presented:
• Latest
achievements
in
narratology
and
novel
plot
recognition
• Specific
expressions
for
describing
tastes
• Automatic
common
sense
ontology
expansion
• Multilanguage
investigation
of
word
ordering
tendencies
• Cognitive
linguistic
approaches
to

metaphor
processing
and
extraction
• Automatic
Cockney
rhyming
slang
processing
for
cyberbullying
detection
• Difficult
questions
were
asked
and
answered:
• “Can
computers
write
poetry?”
• “Can
computers
predict
the
future?”
• Many
topics
related
to
elderly-‐care
solutions:
• Daily
tasks
linguistic
analysis
(pragmatics)
• Therapy
using
communication
bots
• Deeper
understanding
of
user
emotions
in
utterances
• We
could
not
agree
on
importance
and
applicability
of
some
findings,

but
we
concluded
that
if
some
problems
are
still
too
hard,
it
does
not

mean
we
should
change
our
research
interests.
They
must
be
studied,

discussed
and
new
approaches
must
be
explored.
Motivation
•There
was
a
need
of
finding
out
what
is

going
on
in
more
sophisticated
and
less

studied
areas
of
Natural
Language
Processing.

For
that
reason
we
invited
researchers
with

backgrounds
in
computer
science
and

linguistics.
Conclusion
•New
tasks
and
insights
were
learnt
•Possibilities
of
new
NLP
tasks
were

discussed
•Continuation
of
the
Workshop
was

proposed

W13
IJCAI
2016
Workshop
on

AI
for
Synthetic
Biology
Dr.
Fusun
Yaman,
fusun@bbn.com,
BBN
Technologies
Dr.
Aaron
Adler,
aadler@bbn.com,
BBN
Technologies
Dr.
June
Medford,
Colorado
State
University
• Workshop
Highlights
• Synthetic
biology
is
the
systematic
design
and
engineering
of

biological
systems.

• Synthetic
Biology
holds
the
potential
for
revolutionary
advances
in

medicine,
environmental
remediation,
and
many
more
areas.

• Presented
“Introduction
to
Synthetic
Biology”
talk
for
AI
researchers
• Presented
talk
highlighting
the
areas
where
AI
addresses
synthetic

biology
challenges
• Diverse
set
of
talks
on
AI
and
Synthetic
Biology
• MDPs
to
Bayesian
inference
to
deep
reading
to
robotic
laws
• Creating
and
debugging
genetic
circuit
designs
to
metabolomics
to
nano-‐robots
• Brought
together
AI
and
Synthetic
Biology
researchers
• Supported
synthetic
biologists’
travel
to
increase
diversity
at
the
workshop
(thanks
to

the
Bio-‐Design
Automation
Consortium
and
Raytheon
BBN
Technologies)
• Attendees
looking
forward
to
future
workshops
at
AI
venues
Motivation
•Expose
AI
researchers
to
the
Synthetic

Biology
application
domain
•Cross
pollenate
AI
and
Synthetic
Biology

communities
•Develop
collaborations
between
the
two

communities
Conclusion
•Synthetic
Biology
is
a
rich
domain
for
AI
with

many
places
for
AI
to
make
an
impact
•Hopefully
the
first
of
many
workshops
on

this
topic
The
field
has
reached
a
complexity
barrier
that
AI
researchers

can
help
it
overcome.

Site:
http://synthetic-‐biology.bbn.com/ijcai_workshop/

<W14>
IJCAI
2016
Workshop

on
Artificial
Intelligence
for

Knowledge
Management
Site:

http://ifipgroup.com/AI4KMPr
oceedings2016.pdf
• Workshop
Highlights
• 12
papers
and
invited
talk
from
GMU,
Fairfax

• New
perspectives
and
experiences
were
presented,
involving

research
and
companies.
• The

multidisciplinarity,
various
perspectives
and
exciting
challenges

of
Knowledge
Management
was
greatly
appreciated.
• To
progress,
AI
research
should
be
more
connected
to
the
real
and

ambitious
challenges.
• The
selected,
extended
papers
will
be
publish
in
Springer
AICT
series
Motivation
• Demonstrate
the
contribution
of
AI

approaches
and
techniques
to

all
aspects
of

Knowledge
Management

•Share
the
latest
works
in
this
areas
•Set
some
challenges
for
the
Future

Conclusion
•New
perspectives
on
connecting
various
AI

techniques
for
improving
the
process
of

architecturing and
updating
the
knowledge
flow

and
knowledge
discovery
were
presented
and

discussed.
• We
need
more

collaboration
between

symbolic
and
computational
intelligences
and

exploring
the
past
experiences
(i.e.
machine

learning).

<W15>
IJCAI
2016
Workshop
on

Human
Language
Technology
and

Intelligent
Applications
(HLT-‐IA)

Site:
http://aiat.in.th/hltia2016
Workshop
Highlights
• A
proceedings
and
a
thumb
drive
are
prepared

for
each
presenter
and
proceedings
are
given
to

all
participants.
• Five
papers
are
presented
in
the
workshop
with

intensive
discussion
among
participants.
• Presentations
are
various
in
topics,
including

business
intelligence,
social
media
mining,
NLP

resource
development,
sentimental
analysis
as

well
as
big
data
analysis.
Motivation
• Natural
language
processing
(NLP)
is

one
of
the
largest
attractive
area
in

Artificial
Intelligence.

• Recent
modern
methods
are

developed
on
new
applications,
such

as
business
intelligence,
social
media

mining,
sentimental
analysis
as
well

as
big
data
analysis.
Conclusion
• We
have
a
good
discussion
this
time.

• We
plan
to
arrange
the
second

workshop

next
year
at
the
IJCAI
2017

in
Melbourne.
Homepage:
http://aiat.in.th/hltia2016/
Program: http://aiat.in.th/hltia2016/app/webroot/downloads/hltia2016-‐program.pdf
Proceedings: http://aiat.in.th/hltia2016/app/webroot/downloads/hltia2016-‐proceedings.pdf

W18:
IJCAI
2016
Workshop
on
Agent

Mediated
Electronic
Commerce
and

Trading
Agents
Design
and
Analysis

(AMEC/TADA)
http://www.sofiaceppi.com/AMECTADA2016
Workshop
Highlights
• Half
of
accepted
papers
covered
fundamental
topics
such
as:
• Optimal
auctions

• Walrasianequilibria
• Automated
mechanism
design
• Other
half
were
related
to
aspects
of
PowerTAC:

• Prediction
of
energy
demand
profiles

• Dynamic
peak
pricing

• Strategies
for
wholesale
&
tariff
brokers
• Very
engaging
invited
talk
on
Ad
Exchange
Game
(AdX)
by

Mariano
Schain
• Award
ceremony
for
the
two
TAC
2016
tracks:

AdX and
PowerTAC
Background
• Long-‐running
workshop,
co-‐located

usually
with
AAMAS
or
IJCAI
• Focus
on
both
the
theory
and

applications

• Connected
with
the
Trading
Agents

Competition
(TAC)
Conclusion
• Good
quality
submissions

• Lively
discussions
• Continue
collaboration
with
TAC
• Springer
post-‐proceedings
&
potential

Games
special
issue
on
smart
grids

Workshop
Highlights
• 2
invited
speakers:
Pieter
Abbeel
(UCB)
&
Dave
Gunning
(DARPA)
• Papers:
14
(well-‐distributed
among
task
types
addressed)
Motivation
• Most
prior
DL
research
is
on
analysis
tasks
• Fewer
efforts
on
(symbolic)
synthesis
tasks

e.g.,
planning,
scheduling,
design
Objective
• Encourage

research
that
integrates
DL
with

AI
representations
&
techniques
Conclusion
(~125
attendees)
• There’s
great
interest
in
this
topic

• A
follow-‐up
meeting
should
be
held
W20
IJCAI
2016
Workshop
on

Deep
Learning
for
AI
Organizers
• David
W.
Aha,
Co-‐Chair
(NRL)
• Yiannis
Aloimonos
(UMd)
• Andrew
S.
Gordon
(USC)
• Alan
Wagner,
Co-‐Chair
(GTRI)

home.earthlink.net/~dwaha/research/meetings/ijcai16-‐dlai-‐ws
Example
contributions
• Automated
elicitation
of
episodes
from
video
for
navigation
and
near-‐
future
object
prediction
(Kira
et
al.,
2016)
• NAMs
for
learning
&
modeling
conditional
probabilities
of
event
pairs

(for
textual
entailment,
Winograd
schemas)
(Liu
et
al.)
• Integration
of
CNNs
with
tactical
search
for
playing
Go
(Cazenave)

Knowledge-‐based
techniques
for
problem
solving
and
reasoning

(KnowProS 2016)
Organizers:
Roman
Barták,
Lee
McCluskey,
Enrico
Pontelli
http://ktiml.mff.cuni.cz/~bartak/KnowProS2016/
Workshop
Highlights
• A
full
day
workshop
with
10
contributed
talks
and
1

invited
talk
(Veronica
Dahl)
• Presented
topics
(areas)
• Natural
language
processing
• Diagnosis
• Robotics
• Search
• Planning
Will
be
probably
continued
as
a
workshop
or
a

seminar.
Motivation
Bridging
the
gap
between
• knowledge
representation
communities

(focusing
on
expressivity
and
semantics
of

model)
and
• problem
solving
communities
(focusing
on

efficient
problem
solving).
Related
Events
• KEPS (Knowledge
Engineering
for
P&S)
@
ICAPS
• ModRef (Constraint
Modelling
and
Reformulation)
@
CP
• SARA (Symposium
on
Abstraction, Reformulation
and

Approximation)
Workshop
#22

W23
IJCAI
2016
Workshop
on

Multiagent
Path
Finding
Site:

multiagentpathfinding.com
• Workshop
Highlights
• Extensive
review
of
multiagent
pathfinding
algorithms
with

guaranteed
performance,
e.g.
completeness,
path
cost,
polynomial

complexity
• Forming
coherent
groups
can
significantly
reduce
congestion
in
dense

aggregations
of
agents
• Deterministic
multiagent
path
finding
algorithms
can
benefit

significantly
from
randomized
restarts
• Discussion
of
merits
of
finding
optimal
solutions
vs
near-‐optimal
Motivation
•There
has
been
significant
progress
in

multiagent path
finding
since
the
last

workshop
on
the
topic,
especially
in
finding

optimal
or
near
optimal
solutions.
Conclusion
•The
community
has
invented
many
different

approaches
to
solving
the
multiagent path

finding
problem,
but
lack
a
thorough

understanding
of
the
strengths
and

weaknesses
of
each
algorithm

•We
will
develop
a
standard
set
of

benchmarks
for
future
use,
and
test
all

available
algorithms

4th Workshop
on
Sentiment
Analysis

where
AI
meets
Psychology
(SAAIP)
The
Workshop
on
Computational

Modeling
of
Attitudes
(WCMA)
+
Organizing
Committee
(WCMA):
• Mark
Orr,
Virginia
Tech
• Samarth
Swarup,
Virginia
Tech
• Kiran
Lakkaraju,
Sandia
National
Labs
Organizing
Committee
(SAAIP):
• Sivaji
Bandyopadhyay Jadavpur University,
Kolkata
(India)
• Dipankar Das Jadavpur University,
Kolkata
(India)
• Erik
Cambria,Nanyang Technological
University,
Nanyang
(SG)
• Braja Gopal
Patra Jadavpur University,
Kolkata
(India)
Prof. Björn W. Schuller
Professor and Chair, Complex and Intelligent Systems,
University of Passau, Germany.
Reader
(Associate
Professor),
Machine
Learning at

Imperial
College
London, UK.
Permanent
Visiting
Professor,

Harbin
Institute
of
Technology,
Harbin/P.R.
China
Co-‐founding
CEO
of
audEERING
GmbH.
Prof. Russell Fazio
Distinguished Professor of Social
and Behavioral Sciences in the
Department of Psychology
Harold E. Burtt Chair in
Psychology.
Keynote
Speakers:
W24
+
W27

W26:
IJCAI
2016
Workshop
on

Semantic
Machine
Learning
Site: http://datam.i2r.a-‐star.edu.sg/sml16/
Workshop
Highlights
• Well
received
2
Keynotes,
1
Panel
&
4
Paper
presentations;

Attendance:
21+;
Workshop
time:
half
day
• Two
invited
keynotes
highlighted
the
importance
of
unsupervised

learning
and
illustrated
methods
to
formalize
domain
semantics
and

employ
into
the
learning
process.

• Research
paper
presentations
demonstrated
approaches
ranging

from
incorporating
structured
KB’s
into
machine
learning
(and
vice

versa),
to
exploiting
deep
learning
for
domain
semantics.

• People
liked
the
panel
on
challenges
and
potential
directions
to

improve
machine
learning
with
semantics,
and
identified
research

priorities:
knowledge
representation,
evolution
and
validation
of

knowledge
bases,
and
learning
explanation.
• We
could
not
agree
on
clarity
of
the
degree
of
formalizing/expressing

semantics
that
humans
can
interpret
easily
but
machines
cannot.

• Key
Lesson:
“knowledge
should
be
learnable,
and
learning
should
be

explainable.”

Motivation
Identify
research
priorities
for
improving

machine
learning
with
background
knowledge

and
domain
semantics.

Conclusion
• Demonstrated
and
discussed
diverse
ways
to

formalize
and
incorporate
semantics
into

learning,
such
as
machine
translation
via

semantically-‐aware
induction
algorithm.

• Future
work
towards
efficient
knowledge

representation
that
is
employable
into
the

learning
framework.

W28:
4th IJCAI
Workshop
on

Heterogeneous
Information

Network
Analysis
(HINA
2016)
Site:
http://bit.ly/IJCAI-‐HINA-‐2016
• Workshop
Highlights
• 4th iteration
of
workshop;
40+
attendees
over
all
HINA
workshops
• Four
papers
submitted:
three
accepted,
two
presented
• Four
presentations:
one
invited
talk,
two
papers,
one
survey
• Workshop
History:
Past
&
Present
Emphasis
• 1st:
IJCAI
2011,
Barcelona
– 4
papers;
info
sharing,
community
det.
• 2nd:
IJCAI
2013,
Beijing
– 6
papers;
collaborative
classification
• 3rd:
IJCAI
2015,
Buenos
Aires
– 4
papers;
links/text;
soc.
semantic
web
• 4th:
IJCAI
2016,
New
York
– 4
papers;
social
influence,
security
• Announcements
• Proceedings:
to
be
published
online
• Social
Informatics
2016
(http://usa2016.socinfo.eu)
Bellevue,
WA,
USA,
15
– 17
Nov
2016

Workshop
on
Viral
Memetics
(http://bit.ly/SocInfo-‐Viral-‐2016)
• Open
data
repository
&
wiki:
check
back
on
http://bit.ly/IJCAI-‐HINA-‐
2016
• Special
issue:
stay
tuned!
Motivation:
Beyond
Social
Networks
•Path-‐based
similarity
&
relationship
extraction
•Cybersecurity:
information
propagation
&
trust
•Modeling
link
types
&
relationship
strength
•Community
detection
&
formation
modeling
•Collaborative
classification
•Applied
statistical
relational
learning
(SRL)
Summary,
Conclusions,
Future
Work
•Field
is
maturing:
evolution
of
links,
scale
•State
of
the
field
survey:
articles
invited
•Special
issue
of
AI/data
science
journal
planned
•Follow-‐up
workshops:
accepted,
SocInfo 2016
•Open
data:
repositories
&
wiki
(unified)

W30:

IJCAI
2016
Workshop

on
Bioinformatics
and
AI
Site:

http://bioinfo.uqam.ca/IJCAI_BAI2016/
• Workshop
Highlights
• 12
submissions
(7
accepted)
/
3
invited
/
20+
participants
• Keynote
and
Invited
talks
appreciated
by
the
participants
• Biology
inspiring
computation

• Computation
providing
new
insight
in
cancer
studies
• Broad
scope
of
AI
&
Bioinformatics
• ML,
KR,
NLP,
Web&KB-‐IS
• Comparative
genomics,
Proteomics,
Systems
Biology
&
Networks,

• Examples
:
• Extracting
and
integrating
biomedical
data
from
unstructured
sources
• Deep
NN
Language
Models
for
Predicting
Mild
Cognitive
Impairment.

• Scalable
Inference
of
Temporal
Gene
Regulatory
Networks.
• Special
issue
in
Journal
of
Computational
Biology
• Agreement
for
next
Workshop,
to
shed
light
on
personalized

medecine
Motivation
• Bringing
together
researchers
active

on
bioinformatics
and
AI
• Discuss
advances
and
intelligent

practices
in
Computational
Biology
Conclusion
• Progress
in
parallel
of
biological

inspired
computation
and

computational
biology
• More
integration
of
bioinformatics
and

AI
is
needed
in
this
era
of
personalized

medicine.

W32
IJCAI
2016
Workshop
on

Statistical Relational AI
Site:
www.starai.org
• Invited
talks:
ØWilliam
Cohen,
on
TensorLog:

A
Differentiable
Deductive
Database
ØDaniel
Lowd,
on
Adversarial
Statistical
Relational
AI
ØPercy
Liang,
on
Querying
Unnormalized
and

Incomplete
Knowledge
Bases
• 25
accepted
papers,
presented
as
spotlight

talks
and
posters
• Two
Best
Paper
Awards,
sponsored
by
NEC.
ØAnkit
Anand,
Aditya
Grover,
Mausam
and
Parag

Singla.
Contextual
Symmetries
in
Probabilistic

Graphical
Models
ØJay
Pujara
and
Lise
Getoor.
Generic
Statistical

Relational
Entity
Resolution
in
Knowledge
Graphs
Motivation
The
purpose
of
the
Statistical
Relational

AI
(StarAI)
workshop
is
to
bring
together

researchers
and
practitioners
from
two

fields:
logical
(or
relational)
AI
and

probabilistic
(or
statistical)
AI.
Until

recently,
research
in
them
has

progressed
independently
with
little
or

no
interaction.
StarAI
instead
provides
a

big
picture
view
on
AI.
It
is
the
study

and
design
of
intelligent
agents
that
act

in
noisy worlds
composed
of
objects
and
relations among
the
objects.

W33:
IJCAI
2016
Workshop
on
Deep
Reinforcement
Learning:
Frontiers
and
Challenges
Site:
https://sites.google.com/site/deeprlijcai16/
• Workshop
Highlights
• ~120
participants!
• 7
keynote
speakers
covering
various
topics
including
• Deep
RL
for
games
• Deep
RL
for
NLP
• Deep
RL
for
Robotics
• Using
RL
techniques
to
improve
Deep
Learning
• 10
contributed
papers
covering
various
topics
including
• Hierarchical
Deep
RL
• Deep
RL
for
more
challenging
games
like
Minecraft
• Model
based
DRL
• Learning
to
communicate
to
solve
riddles
• Dynamic
neural
Turing
Machines
• Panel
discussion
on
research
challenges
in
Deep
RL.
Motivation
• Deep
RL
is
an
exciting
research
field
in

ICML/NIPS
community.
Main
motivation

of
this
workshop
is
to
involve
IJCAI

community
in
this
research
drive.
• Integrating
Deep
Learning
and

Reinforcement
Learning.
• Workshop
focused
on
both
DL
for
RL
and

RL
for
DL.
Conclusion
• Important
research
challenges
in
the

future
• Transfer
learning
in
Deep
RL.
• New
architectures
for
Deep
RL.
• Data
efficient
Deep
RL.
• Deep
RL
for
NLP.
• AI
community
should
take
this
up
and
we

look
forward
for
more
future
meetings.

W34
IJCAI
2016
Workshop
on
Natural

Language
Processing
for
Social
Media

(SocialNLP
2016)
Site:
https://sites.google.com/site/socialnlp2016/

• Workshop
Highlights
• Prof.
Yuheng
Hu
(University
of
Illinois
at
Chicago)
delivered
an

excellent
keynote
speech
on
event
analysis
in
social
media.
His
talk

received
great
feedback
and
brought
lively
discussions
among
the

participants
on
the
insights
of
people’s
engagement
with
events
and

the
tweeting
behaviors
during
engaged
events.
• Sentiment
analysis
using
AI,
especially
machine
learning
techniques,

is
one
of
the
mainstream
topics
on
SocialNLP.
• Deep
learning
was
mentioned
by
every
presentation!

• Due
to
the
importance
of
benchmark
datasets,
SocialNLP
encourages

DATA
papers
to
share
resource/data
creation
and
preliminary
analysis.

Two
interesting
DATA
track
papers
were
accepted
this
year,
one
on

Hindi-‐English
Mixing,
and
another
on
Moroccan
Arabic
code
switching.
• As
the
fourth
SocialNLP
workshop,
we’ve
maintained
a
modest
size

with
6
full
papers
presentations
and
a
total
of
20-‐25
participants.
• The
organizers
would
like
to
thank
all
SocialNLP@IJCAI
workshop

attendees
for
their
active
participation
in
the
Q&A
session
following

the
talks,
creating
many
interactive
and
intensive
discussions.
• We
look
forward
to
seeing
you
at
SocialNLP@EMNLP
2016.
Motivation
• To
enhance
social
computing
with
AI
and
NLP
• To
solve
NLP
problems
using
information

extracted
or
learned
from
social
networks
and

social
media
• To
address
new
problems
related
to
both
social

computing
and
natural
language
processing
Conclusion
• Event
detection
and
sentiment
analysis
are
hot

topics
in
SocialNLP research.
• Data
sparsity
is
a
key
challenge
due
to
the

nature
of
short
texts
on
social
media.
• Deep
learning
for
SocialNLP is
gaining
popularity

and
we
expect
to
see
many
promising
results.
• Improved
publicity
is
in
order
-‐-‐ participants

enjoyed
the
quality
presentations
at
the
workshop.

IJCAI2016
– W36
29th Int.
Workshop
on
Qualitative
Reasoning(QR2016)
Site:
https://ivi.fnwi.uva.nl/tcs/QRgroup/qr16/index.html
Motivation
Understanding
the
world
from

incomplete,
imprecise,
and/or

uncertain
data,
realised
as

cognitive
systems
capable
of

knowledge-‐‑level
interaction
(with

humans
in
the
loop).
Conclusion
Contemporary
challenges
concern

multidimensional
problems,

which
require
semantic

interoperability
of
miscellaneous

representations
and
algorithms.
Workshop
Highlights
• Invited
talk:
Qualitative
spatial
reasoning
– Diedrich Wolter
• 14
stimulating
contributions
(see
reviewed
papers online)
New
ideas
on:
• Qualitative
spatial
reasoning
(numerous
application
areas)
• Conceptual
modeling
and
simulation
for
education
(learning)
• Diagnosis
and
decision-‐‑making,
e.g.
environmental
problems
• Explanatory
models
for
health,
biodegradation
and
science
• Order
of
magnitude
reasoning
(for
business
and
marketing)
• Human
and
physical
robot
interaction
during
gaming

W37
IJCAI
2016
5th
Workshop
on
Human-‐Agent
Interaction
Design
and
Models

Site:
http://haidm.wordpress.com

Why
HAIDM?
●Bring
together
researchers
from
HCI,
AI,
ML
and
robotics.
●Define
challenges
at
intersection
of
disciplines.
●Exchanges
of
methodologies
results
and
insights
Highlights
over
the
years
●Invited
talks
by
leaders
in
the
field:
John
Gratch,
Eric
Horvitz,
●Spawned
collaborations
and
applications
in
novel
domains
(smart
cities,
citizen

science,
etc…).
●Sponsored
by
two
EU
large
scale
projects

W39
IJCAI
2016
Workshop
on

Interactions
with
Mixed
Agent

Types
(Agent-‐Mix)
Site:
http://ccc.inaoep.mx/inmat

• Workshop
Highlights
• Half-‐day
workshop
featuring
7
talks
from
authors
of
invited
and

submitted
papers
• Interactive
setting
with
an
emphasis
on
incisive
discussions

pertaining
to
each
paper
• Presenters
appreciated
the
detailed
feedback
that
they
received,

which
should
help
guide
their
future
investigations
• Methods
presented
in
the
talks
could
be
grouped
into
two
broad

themes
of
opponent
modeling,
and
planning
and
optimization
• Domains
utilized
in
the
talks
included
bounty
hunting,
repeated

games
with
non-‐stationary
opponents,
strategic
path
planning,

security
games
among
others
Motivation
• As
AI
becomes
ubiquitous,
there
is
an
urgent

need
to
build
software
and
devices
that
can

reliably
interact
with
other
intelligent
agents
• Such
software
will
most
likely
encounter
agents

that
deviate
from
optimality
or
rationality
and

whose
objectives,
learning
dynamics
and

representation
of
the
world
are
usually

unknown
• Agent-‐Mix
workshop
seeks
to
improve
our

understanding
of
how
agents
should
interact
in

a
heterogeneous
world
Conclusion
• Research
is
gradually
considering
a
variety
of

interacting
agents

• Methods
are
needed
to
close
the
gap
between

the
state
of
the
art
and
heterogeneous
MAS
• There
is
a
need
to
assemble
diverse

perspectives
to
promote
a
robust

understanding
of
Agent-‐Mix

W41:
Closing
the
Cognitive

Loop
(CogComp16)

researcher.watson.ibm.com/researcher/view_
group.php?id=6501
Workshop
Highlights
• Various
real-‐world
applications
of
AI were
presented:

• Cognitive
assistance
for
data
science
• Human-‐Robot
collaboration
• Intelligent
control
of
crowdsourcing
applications
• Intelligence
analysis
for
security
and
law
enforcement
• Incorporating
intuition
into
sensory
interpretation
for
vision
• Interaction
issues:
• Each
application
had
a
unique
set
of
interaction
challengesto

overcome
to
accommodate
humans
in
the
loop
• Two
modes
of
interaction:
1. Extract
knowledge:
Use
human
expertise
and
knowledge
of
a

given
application
domain
to
help
the
machine
2. Present
decisions:
Design
interfaces
to
effectively
present
team

decisions
and
solicit
feedback
• Problem
Pillars for
Human-‐Aware
AI:
• Explanation of
decisions
• Interpretability of
decision
process
• Efficient
and
time-‐sensitive
context
transfer
• Division
of
labor
and
skills
• Legal
and
ethical
issues

Motivation
• Key
Idea:
Human-‐Machine
teams
can

achieve
better
performance
than
either

alone – augmented
intelligence
• What
are
the
key
issues
to
address
in

order
to
accommodate
humans
as
first-‐
class
citizens
in
the
decision-‐making

loop/processof
AI
systems?
Conclusion
• Current
Workshop:
Mostly
application

oriented,
with
narrow
human-‐in-‐the-‐loop

issues
for
each
application
• Next
Workshop:
What
are
the
general

problem
pillarsthat
AI
practitioners
must

understand
and
support
to
enable

human-‐aware
AI?

Summaries of Workshops held at IJCAI 2016 at New York in July

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Summaries of Workshops held at IJCAI 2016 at New York in July