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/ijcai2016ws.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 Reasoninghttp://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 reasonabout other agents’ goals.

• Goal reasoning algorithm control for $100K UUV test fielded.

• Select papers to be published in AI Communications.

MotivationGoal structures can help manage long-‐term behavior, anticipate the future, select among priorities, and adapt to surprise.

ConclusionNew insights:

• A strong affinity with BDI systems existsNew 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 AnalysisSite: 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 StoreRecommendation with Budget Constraints”

• 10kUSD 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

behaviors of other users.

•The discovery of influencers is a complex problemthat requires developing models, techniques

andalgorithms for an appropriate analysis of thecurrent social network.

Conclusion•Research gaps in the field were identified.•Interesting discussions were generated about

possible approaches to social influenceanalysis.

W06 IJCAI 2016 Workshop on Ethics for Artificial IntelligenceSite:<https://www.cs.ox.ac.uk/efai>


• 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

MotivationIn 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 naturalnessmay 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 MachinesSite:sites.google.com/site/ijcai2016iml


• 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

MotivationlPreferences are a central concept of decision making and used in fields including AI, databases, and human-‐computer interaction

lThis workshop brings together researchers from numerous sub-‐fields, who are interested in computational aspects of preference handling

lAim: Report on novel and emerging research on preferences and provide an opportunity for cross-‐fertilization between fields

ConclusionlNoteworthy progress in established areas including voting, databases, and knowledge representation and reasoning

lNew 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 Highlightsv 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/posterpresentation.

v 5 Invited Plenary Speakers + Panel Discussion.

v Best Paper Awards sponsored byMicrosoft 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 novelmachine learning and optimization algorithms.

Motivation• A compelling demand for novel machine learning, data

mining and optimization algorithms to specifically tacklethe unique challenges associated with biomedical andhealthcare data.

• Recentmajor breakthroughs inmachine learning that isequipped with powerful optimization technologies(deep learning, etc.)

• Idea exchanges among applied mathematicians,computer scientists, bioinformaticians, computationalbiologists, industrial engineers, clinicians and healthcareresearchers.

See You At Next BOOM!

W12 IJCAI 2016 Workshop on Language Sense on Computers

Organizers:Akinori Abe & RafalRzepkahttp://ultimavi.arc.net.my/ave/IJCAI2016/


• 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 BiologyDr. Fusun Yaman, [email protected], BBN TechnologiesDr. Aaron Adler, [email protected], BBN TechnologiesDr. 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 ManagementSite: http://ifipgroup.com/AI4KMProceedings2016.pdf


• 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.pdfProceedings: 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 AIOrganizers• 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)

<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.pdfProceedings: http://aiat.in.th/hltia2016/app/webroot/downloads/hltia2016-‐proceedings.pdf

Knowledge-‐based techniques for problem solving and reasoning (KnowProS 2016)Organizers: Roman Barták, Lee McCluskey, Enrico Pontellihttp://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.

MotivationBridging 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 FindingSite: multiagentpathfinding.com


• 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. SchullerProfessor 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. ChinaCo-‐founding CEO of audEERING GmbH.

Prof. Russell FazioDistinguished Professor of Socialand Behavioral Sciences in theDepartment of Psychology

Harold E. Burtt Chair inPsychology.

Keynote Speakers:

W24 + W27

W26: IJCAI 2016 Workshop on Semantic Machine LearningSite: 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.”

MotivationIdentify 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 AISite: http://bioinfo.uqam.ca/IJCAI_BAI2016/


• 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 AISite: www.starai.org

• Invited talks:ØWilliam Cohen, on TensorLog: A Differentiable Deductive Database

ØDaniel Lowd, on Adversarial Statistical Relational AIØPercy Liang, on Querying Unnormalized and Incomplete Knowledge Bases

• 25 accepted papers, presented as spotlight talks and posters

• Two Best Paper Awards, sponsored by NEC.ØAnkit 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

MotivationThe 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 objectsand relations among the objects.

W33: IJCAI 2016 Workshop on Deep Reinforcement Learning: Frontiers and Challenges

Site: https://sites.google.com/site/deeprlijcai16/


• ~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/


• 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 – W3629th Int. Workshop on Qualitative Reasoning (QR2016)

Site: https://ivi.fnwi.uva.nl/tcs/QRgroup/qr16/index.html

MotivationUnderstanding the world from incomplete, imprecise, and/or uncertain data, realised as cognitive systems capable of knowledge-‐‑level interaction (with humans in the loop).

ConclusionContemporary 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


• 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 AIwere 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 challenges to

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 machine2. 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 pillars that AI practitioners must understand and support to enable human-‐aware AI?