Thirty-Second Conference on Artificial Intelligence
February 2-3, 2018
New Orleans, Louisiana, USA
What Is the Tutorial Forum?
The Tutorial Forum provides an opportunity for researchers and practitioners to spend two days each year exploring exciting advances in disciplines outside their normal focus. We believe this type of forum is essential for the cross fertilization, cohesiveness, and vitality of the AI field. We all have a lot to learn from each other; the Tutorial Forum promotes the continuing education of each member of AAAI.
Half-day tutorials are 4 hours, including breaks; quarter-day tutorials are one hour and 45 minutes with no break. Quarter-day tutorials are denoted by a ‘Q’ at the end of the tutorial code.
Friday, February 2
9:00 AM – 1:00 PM
- FA1: Adversarial Machine Learning – Bo Li, Dawn Song, Yevgeniy Vorobeychik
Tutorial Materials: https://aaai18adversarial.github.io/
2:00 – 6:00 PM
- FP1: A Design Hackathon for “AI for Social Good” K-12 Outreach – Yolanda Gil, Tara Chklovski, Rusty Nye, Allison Colyer
Tutorial Materials: http://iridescentlearning.org/ai-for-social-good-design-hackathon-aaai-2018/
- FP2: A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress – Prashant Doshi, Saurabh Arora
Tutorial Materials: http://thinc.cs.uga.edu/AAAI18.html
- FP3: Bandit Problems and Algorithms – Csaba Szepesvari, Tor Lattimore
- FP4: Deep Learning Models for Health Care: Challenges and Solutions – Edward Yoonjae Choi, Sanjay Purushotham, Yan Liu, Jimeng Sun
- FP5: Network Representation Learning: Enabling Network Inference in Vector Space – Peng Cui, Wenwu Zhu
4:15 – 6:00 PM
- FP6Q: Constraint Learning – Luc De Raedt, Andrea Passerini, Stefano Teso
Tutorial Materials: https://dtai.cs.kuleuven.be/constraint%20learning%20tutorial%20aaai18
4:30 – 6:15 PM
- FP7Q: Game Theory for Data Science: Eliciting High-Quality Information – Boi Faltings, Goran Radanovic
Tutorial Materials: http://liapc3.epfl.ch/elicitation-aaai2018/
Saturday, February 3
9:00 AM – 1:00 PM
- SA1: Cognitive Vision – On Deep Semantics in Visuo-Spatial Computing – Mehul Bhatt, Jakob Suchan
Tutorial Materials: http://hcc.uni-bremen.de/cognitive-vision/index.php/events
- SA2: Computational Solutions against Fake News: AI vs. DB Perspectives – Naeemul Hassan, Dongwon Lee
Tutorial Materials: https://john.cs.olemiss.edu/~nhassan/file/aaai2018tutorial.html
- SA3: Modelling Planning Domains – Roman Barták, Lukas Chrpa
Tutorial Materials: http://ktiml.mff.cuni.cz/~bartak/AAAI2018/
- SA4: Rulelog: Highly Expressive Semantic Rules with Scalable Deep Reasoning Networks – Benjamin Grosof, Michael Kifer, Paul Fodor, Janine Bloomfield
Tutorial Materials: http://benjamingrosof.com/misc-publications/#AAAI18RulelogTutorial
- SA5: Scalable Deep Learning using CNTK – Yuqing Tang, Sayan Pathak
- SA6: Strategic, Online Learning, and Computational Aspects of Social Network Science – Ramasuri Narayanam, Yadati Narahari
Tutorial Materials: http://researcher.watson.ibm.com/researcher/view_person_subpage.php?id=8494
8:15 – 10:00 AM
- SA7Q: Recent Advances in Structured Prediction – Jana Doppa, Liping Liu, Chao Ma
10:15 AM – 12:00 PM
- SA9Q: Responsible Artificial Intelligence – Virginia Dignum
Tutorial Materials: https://sites.google.com/view/responsible-ai-tutorial/home
11:15 AM – 1:00 PM
- SA10Q: Multi-Agent Distributed Constrained Optimization – Ferdinando Fioretto, William Yeoh, Roie Zivan
Tutorial Materials: http://www-personal.umich.edu/~fioretto/cfp/AAAI18/index.html
2:00 – 6:00 PM
- SP1: Artificial Intelligence and Games – Julian Togelius
- SP2: Cognitive Robotics in Industrial Settings Competition – William Harrison III, Erez Karpas, Zeid Kootbally, Tim Niemueller, Craig Schlenoff, Eric Timmons, Tiago Vaquero
Tutorial Materials: https://www.niemueller.org/event/aaai2018-tutorial/
- SP3: Integrating Learning into Reasoning – Brendan Juba, Loizos Michael
Tutorial Materials: http://cognition.ouc.ac.cy/learning
- SP4: Knowledge Graph Construction from Web Corpora – Mayank Kejriwal, Craig Knoblock, Pedro Szekely
Tutorial Materials: http://usc-isi-i2.github.io/AAAI18Tutorial/
- SP5: Machine Reading for Precision Medicine – Hoifung Poon, Chris Quirk, Scott Wen-Tau Yih
Tutorial Materials: https://www.microsoft.com/en-us/research/uploads/prod/2018/01/1802_aaai-tutorial_precision-med.pdf
- SP6: When Advanced Machine Learning Meets Intelligent Recommender Systems – Liang Hu, Longbing Cao, Jian Cao, Songlei Jian
Tutorial Materials: https://sites.google.com/view/lianghu/home/tutorials
2:00 – 3:45 PM
- SP7Q: Recent Advances and Applications in Markov Logic Networks – Deepak Venugopal, Vibhav Gogate, Vincent Ng
Tutorial Materials: https://sites.google.com/site/aaai18tutorial/
4:15 – 6:00 PM
- SP8Q: Implementing Motivation and Emotion in AI Architectures – Joscha Bach
Tutorail Materials: http://bach.ai/pub/AAAI2018%20Modeling%20emotion%20and%20motivation%20tutorial.pdf
- SP9Q: AI Techniques for Price Prediction in Commodity Markets – Ramasuri Narayanam, Rohith Vallam, Ritwik Chaudhuri, Manish Kataria, Gyana Parija, Fatemeh Jahedpari
Tutorial Materials: http://researcher.watson.ibm.com/researcher/view_person_subpage.php?id=8492
FA1: Adversarial Machine Learning
Bo Li, Dawn Song, Yevgeniy Vorobeychik
Machine learning has seen a remarkable rate of adoption in recent years across a broad spectrum of industries and applications. Many applications of machine learning techniques are adversarial in nature, insofar as the goal is to distinguish instances which are “bad” from “good.” Indeed, adversarial use goes well beyond this simple classification example: forensic analysis of malware which incorporates clustering, anomaly detection, and even vision systems in autonomous vehicles could all potentially be subject to attacks. In response to these concerns, there is an emerging literature on adversarial machine learning, which spans both the analysis of vulnerabilities in machine learning, and algorithmic techniques which yield more robust learning.
This tutorial will survey a broad array of these issues and techniques from both cybersecurity and machine learning research areas. In particular, we consider problems of adversarial classifier evasion, where the attacker changes behavior to escape being detected, and poisoning, where training data itself is corrupted. We discuss both evasion and poisoning attacks, first on classifiers, and then on other learning paradigms, and the associated defensive techniques. We then consider specialized techniques for both attacking and defending neural network, particularly focusing on deep learning techniques and their vulnerabilities to adversarially crafted instances.
UC BerkeleyBo Li is a postdoctoral researcher in EECS at UC Berkeley. Her research interest lies in adversarial deep learning, security, privacy, and game theory. She was a recipient of a Symantec Research Labs Graduate Fellowship. She obtained her Ph.D. degree from Vanderbilt University in 2016.
UC BerkeleyDawn Song is a Professor at UC Berkeley. Her research lies in deep learning and security. She is the recipient the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan-Research-Fellowship, MIT Technology Review TR-35 Award, George Tallman Ladd Research Award, Okawa Foundation Research Award, Faculty Research Award from IBM, Google and other major tech companies.
Vanderbilt UniversityYevgeniy Vorobeychik is an Assistant Professor of Computer Science at Vanderbilt University. His work focuses on game theoretic modeling of security and privacy, adversarial machine learning, and algorithmic and behavioral game theory. He received an NSF CAREER award and an honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award.
FP1: A Design Hackathon for “AI for Social Good” K-12 Outreach
Yolanda Gil, Tara Chklovski, Rusty Nye, Allison Colyer
AI Design Challenge Hackathon hosted by Iridescent
AI is not new in the research world, but for the public, suddenly the topic of AI is everywhere. The perception of AI driven machines can be negative and fear-based. As a result, there are real risks that the value of the technology is not understood, and of negative consumer and regulatory backlash. This perception is more prevalent in underserved communities who lack access to high quality STEM education. With 50% of children in the US belonging to minority groups in two years, there is a real danger that the “digital divide” could widen further into an “AI divide.”
But, we also have a unique opportunity to play this right from the very start. As researchers, we can actively share the problems we tackle with the broader public, and give them a glimpse into what AI really is – and how AI can strengthen our communities and societies.
This tutorial will be a fun, hands-on, team-based experience in which you will work in teams, learn how to take complex topics, abstract concepts and develop tangible, open-ended, hands-on design challenges that will help people develop an intuitive understanding of the same concepts. For instance, students can be challenged to build a physical sorting machine that processes different types of physical materials in parallel. This gives them an intuitive understanding of parallel processing. Or they could develop a circuits-signals game that gives them an intuitive understanding of how autonomous vehicles process signals.
This experience will not only help you inspire the broader public, but it will help you look at your research through a different lens.
“People think I’m a genius for understanding Reynolds number on such a fundamental level, but really, I know it because I taught it to a bunch of 4th graders.” – Madeline Foster, Graduate Student UC Berkeley
The workshop will be led by Iridescent — a global engineering and technology education nonprofit, that has been supported by Google, NVIDIA, GM, Adobe, Salesforce to bring cutting-edge technologies to underserved communities across the US and worldwide.
Following the workshop, on Saturday, you will have the unique opportunity to take an exciting field trip to a local New Orleans school, and practice your newly acquired skills of story-telling with underserved students and parents. You will be invited to inspire them with your personal story and connection to AI, and help them develop a deeper understanding of some fundamental AI concepts. This will be the very first time AI researchers will communicate cutting-edge concepts in hands-on ways with hundreds of underserved children and parents.
More information: http://iridescentlearning.org/ai-for-social-good-design-hackathon-aaai-2018/
University of Southern CaliforniaYolanda Gil is Director of Knowledge Technologies at the Information Sciences Institute, and Research Professor in Computer Science at the University of Southern California. Her recent research focuses on automating scientific discovery. She is interested in making AI more accessible for everyone, and teaches courses on data science for non-programmers. She received her PhD in computer science from Carnegie Mellon University.
IridescentTara Chklovski is the Founder and CEO of the global education non-profit Iridescent. Tara founded Iridescent in 2006 to empower underrepresented youth, especially girls, to become innovators and leaders using engineering and technology. Iridescent has since grown to a community of over 7,000 STEM professional mentors and 90,000 participants across 100 countries through its flagship programs Curiosity Machine and Technovation. Forbes highlighted Tara in 2016 as “the pioneer empowering the incredible tech girls of the future” and Discovery’s Science Channel showcased her as their inaugural CEO Science Super Star Hero. She has presented Iridescent’s work at the White House STEM Inclusion Summit and at the United Nations Empowering Women Summit.
IridescentRusty Nye (Manager of Engineering Content Development) is a curriculum developer and educator with more than ten years of experience in informal education. He works alongside scientists and engineers to develop content to introduce engineering and technology to students. He is the author of ‘Making Dinos: an engineering adventure notebook’, a physics and biomechanic workbook for children.
IridescentAllison Colyer (Engineering Curriculum Developer) has background in chemical engineering and studied at Cooper Union. She started working in education four years ago after volunteering to teach science in her local community. She has developed curriculum around a variety of topics, such as artificial intelligence, nanoscience, electronics, biology, genetics, and mobile app development.
FP2: A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Prashant Doshi, Saurabh Arora
Inverse reinforcement learning (IRL) seeks to find the preferences of another agent using its observed behavior, thereby avoiding a manual specification of its reward function. IRL is appealing because of its ambitious potential to use data recorded in everyday tasks (e.g., driving data) to build autonomous agents capable of modeling human behavior and socially collaborating with others in our society. Research related to IRL has grown tremendously in recent years because the reward function is inherently more transferable compared to the observed agent’s control policy, and IRL has potentially ground-breaking applications such as autonomous vehicle control, predicting the future behavior of the demonstrator to facilitate multi-agent decision making. This tutorial will provide a comprehensive and unified review from early research to current methods as well as open questions in IRL. The tutorial requires no prior knowledge of IRL but assumes a familiarity with basic probability and statistics.
University of GeorgiaPrashant Doshi is a tenured Professor of Computer Science and faculty fellow of the AI Institute at The University of Georgia, USA. His research interests lie broadly in artificial intelligence and robotics. Specifically, he studies automated decision-making under uncertainty in multiagent settings, non-cooperative game theory, and robot learning, specifically inverse reinforcement learning. He was a visiting professor at the University of Waterloo in 2015, and he has also had short stints at the IBM T. J. Watson Research Center. He has published 125+ articles and papers in journals, conferences, and other forums in the fields of agents and AI. His research has led to publications in the Journal of AI Research, Journal of AAMAS, AAAI, IJCAI and AAMAS conferences. In 2011, Prof. Doshi was awarded UGA’s Creative Research Medal for his contributions to automated decision making. Prof. Doshi teaches introductory courses on AI and Robotics to undergraduate and graduate students, and a course on decision making under uncertainty to graduate students, all of which are well received among the students.
University of GeorgiaSaurabh Arora is a Ph.D student in Computer Science Department at The University of Georgia. His research-work focuses on ‘on-line inverse reinforcement learning under occlusion.’ Prashant and Saurabh are co-writing a comprehensive survey article on IRL that will be submitted to AI Journal shortly.
FP3: Bandit Problems and Algorithms
Csaba Szepesvari, Tor Lattimore
Decision making in the face of uncertainty is a significant challenge in machine learning. Which drugs should a patient receive? How should I allocate my study time between courses? Which version of a website will return the most revenue? What move should be considered next when playing chess/go? All of these questions can be expressed in the multi-armed bandit framework where a learning agent sequentially takes actions, observes rewards and aims to maximize the total reward over a period of time. The framework is now very popular, used in practice by big companies, and growing fast. The focus of the tutorial will be on understanding the statistical ideas, mathematics and implementation details of the core algorithmic concepts.
The tutorial will cover enough of the introductory material to give researchers a starting point to the field and practitioners an overview of what is available and how the bandit framework might be applicable to their problems. Attendees should come away with a clear understanding of the core challenges in bandit problems and some of the approaches used to address them.
The required knowledge is limited to basic probability.
University of AlbertaCsaba Szepesvari is a new proud grandfather, researcher at DeepMind and Professor of Computing Science at the University of Alberta. With 30 years of experience in ML, he has published two books and ~200 peer-reviewed articles, many on bandit algorithms. He is best known as co-inventor of UCT, a popular tree-search algorithm.
University of AlbertaTor Lattimore is a researcher at DeepMind. Before that he was a postdoctoral fellow at the University of Alberta with Csaba Szepesvari and briefly an assistant professor at Indiana University. His recent work has focussed on bandit algorithms and sequential decision making in the face of uncertainty.
FP4: Deep Learning Models for Health Care: Challenges and Solutions
Edward Yoonjae Choi, Sanjay Purushotham, Yan Liu, Jimeng Sun
It is widely believed that deep learning and artificial intelligence techniques will fundamentally change healthcare industries. Even though recent development in deep learning has achieved successes in many applications, such as computer vision, natural language processing, speech recognition and so on, healthcare applications pose many significantly different challenges to existing deep learning models. Examples include but not are limited to interpretations for prediction, heterogeneity in data, missing value, multi-rate multi-resolution data, big and small data, and privacy issues. In this tutorial, we will discuss a series of problems in healthcare that can benefit from deep learning models, the challenges as well as recent advances in addressing those. We will also include data sets and demos of working systems.
- Introduce diverse types of healthcare data
- Overview different ML problems for healthcare applications
- Explain the technical challenges for applying deep learning for healthcare applications
Recently, there are substantially growing interest in ML in health applications, thanks to the heterogeneous healthcare data such as electronic health records, medical images, clinical notes and continuous monitoring data. The analytic problems in healthcare pose many unique ML challenges that are worth discussing.
Georgia TechEdward Choi is a PhD student at Georgia Tech advised by Dr. Jimeng Sun. His research is focused on analyzing longitudinal electronic health records with machine learning techniques. His works include representation learning for healthcare concepts, interpretable sequence prediction, and synthetic EHR generation.
University of Southern CaliforniaSanjay Purushotham is a Postdoctoral researcher in the Department of Computer Science at the University of Southern California (USC). His research interests are in machine learning, data mining, and its applications to healthcare & bio-informatics. Recently, he has been developing deep learning solutions for healthcare time series analysis.
University of Southern CaliforniaYan Liu is an Associate Professor in Computer Science Department at the University of Southern California from 2010. Before that, she was a researcher at IBM from 2006-2010. Her research interests are developing scalable machine learning algorithms with applications to health and biology applications, social media analysis, and climate modeling.
Georgia TechJimeng Sun is an Associate Professor of College of Computing at Georgia Tech. Prior to Georgia Tech, he was a researcher at IBM TJ Watson Research Center. His research focuses on health analytics and data mining, especially in designing tensor factorizations, deep learning methods, and large-scale predictive modeling systems.
FP5: Network Representation Learning: Enabling Network Inference in Vector Space
Peng Cui, Wenwu Zhu
Nowadays, larger and larger, more and more sophisticated networks are used in more and more applications. It is well recognized that network data is sophisticated and challenging. To process graph data effectively, the first critical challenge is network data representation, that is, how to represent networks properly so that advanced analytic tasks, such as pattern discovery, analysis and prediction, can be conducted efficiently in both time and space. In this tutorial, we will review the recent thoughts and achievements on network embedding. More specifically, a series of fundamental problems in network embedding will be discussed, including why we need to revisit network representation, what are the research goals of network embedding, how network embedding can be learned, and the major future directions of network embedding.
Tsinghua UniversityPeng Cui is an associate professor in Tsinghua University. His research interests include network representation learning, data-driven causal analysis and social dynamics modeling. He has published more than 80 papers in prestigious conferences and journals in data mining and multimedia. He has been associate editors of Associate Editors of IEEE TKDE, ACM TOMM, Elsevier Journal on Neurocomputing etc.
Tsinghua UniversityWenwu Zhu is a full professor in Tsinghua University. He is an IEEE Fellow, SPIE Fellow and ACM Distinguished Scientist. He has published over 200 referred papers in the areas of multimedia computing, communications and networking. He is now the EiC of IEEE TMM, and served(s) on various editorial boards, such as associate editor for IEEE Transactions on Mobile Computing, IEEE TCSVT etc.
FP6Q: Constraint Learning
Luc De Raedt, Andrea Passerini, Stefano Teso
Constraints are ubiquitous in Artificial Intelligence and Operations Research, they appear in purely logical problems such as propositional satisfiability, constraint satisfaction problems, and full-fledged constraint optimization. Constraint learning is required when the structure and/or the parameters of the target constraint satisfaction (or optimization) problem are not known in advance, and must be learned. Potential sources of supervision include offline data and other oracles, e.g. human domain experts and decision makers. Despite the relevance of constraint learning to Artificial Intelligence, no general introduction to the field of constraint learning is available. This tutorial aims at filling this gap.
This tutorial is intended for Artificial Intelligence researchers and practitioners, as well as domain experts interested in constraint learning, programming, modelling, and satisfaction. The participants will gain an understanding of the core concepts in constraint learning, as well as a conceptual map of the variety of methods available and of the relationships between them. The main goal is to prepare the audience to understand the field of constraint learning and its relation to the broader context of machine learning and constraint satisfaction.
Luc De Raedt
Director of the lab for Declarative Languages and AI at the KULeuven in Belgium
Post-doc in the lab for Declarative Languages and AI at the KULeuven in Belgium
Associate Professor at the University of Trento in Italy
FP7Q: Game Theory for Data Science: Eliciting High-Quality Information
Boi Faltings, Goran Radanovic
AI systems often depend on information provided by other agents, for example sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards.
This tutorial is intended for AI researchers and practitioners working with contributed or crowdsourced data, be it in machine learning, AI on the web, sensing and computational sustainability, or optimization. We will base the tutorial on the book “Game Theory for Data Science” that has just appeared at Morgan Claypool publishers, and focus on in-depth explanation of the principles.
We will cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews and predictions. We will survey different incentive mechanisms, including proper scoring rules, prediction markets and peer consistency, Bayesian Truth Serum, Peer Truth Serum, Peer Truth Serum for Crowdsourcing, and Correlated Agreement. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing and peer grading, and address issues that arise when using them in a decentralized machine learning system.
EPFLBoi Faltings is a full professor at EPFL and has worked in AI since 1983. He has been one of the pioneers on the topic of mechanisms for truthful information elicitation, with the first work dating back to 2003. He has taught AI and multi-agent systems to students at the Swiss Federal Institute of Technology for 30 years. He is a fellow of AAAI and ECCAI.
Harvard UniversityGoran Radanovic is a post-doctoral fellow at Harvard University. He obtained his Ph.D. degree at the Swiss Federal Institute of Technology and has worked on the topic of mechanisms for information elicitation since 2011. His work has been published mainly at AI conferences.
SA1: Cognitive Vision – On Deep Semantics in Visuo-Spatial Computing
Mehul Bhatt, Jakob Suchan
This tutorial presents cognitive vision from the perspectives of language, logic, and artificial intelligence. The tutorial focusses on application areas where explainability and semantic interpretation of dynamic visuo-spatial imagery are central, e.g., for commonsense scene understanding; vision for robotics and HRI; narrative interpretation from the viewpoints of visuo-auditory perception & digital media, multimodal sensemaking of data.
We particularly highlight Deep (Visuo-Spatial) Semantics, denoting the existence of systematic formalisation and declarative programming methods -e.g., pertaining to space and motion- supporting query answering, relational learning, non-monotonic abductive inference, and embodied simulation. Here, we particularly demonstrate the integration of methods from knowledge representation and computer vision with a focus on reasoning & learning about space, action, and change. In the backdrop of areas as diverse as architecture design, cognitive film studies, cognitive robotics, and eye-tracking, this tutorial covers both applications and basic methods concerned with topics such as: explainable visual perception, semantic video understanding, language generation from video, declarative spatial reasoning, and computational models of narrative.
Prerequisites: No special background needed; participants need only be generally interested in AI, Cognitive Science, or HCI. We especially encourage early doctoral researchers, and educators wanting to learn about general tools for logic-based reasoning about visual imagery.
Örebro University and University of BremenMehul Bhatt is Professor within the School of Science and Technology at Örebro University (Sweden), and Professor at the Department of Computer Science, University of Bremen (Germany). His research interests lie at the intersection of artificial intelligence, cognitive science and HCI, focussing on knowledge representation, visuo-spatial cognition, and design cognition.
University of BremenJakob Suchan is research assistant within the Human-Centred Cognitive Assistance Lab at the Department of Computer Science, University of Bremen. His research focusses on the integration of vision and KR from the viewpoint of computational cognitive systems where integrated (embodied) perception and interaction are involved. www.cognitive-vision.org
SA2: Computational Solutions against Fake News: DB vs. AI Approaches
Naeemul Hassan, Dongwon Lee
The problem of Fake News is arguably one of the most serious challenges facing the media industry and a threat to democratic societies worldwide. By exploiting the current infrastructure of social media platforms where contents can be created and disseminated to a large audience with a little to zero cost, and the click-through-rate based revenue model of the media ecosystem, the problem has reached to an unprecedented level. To mitigate such fake news spread, researchers from multiple disciplines have proposed various strategies, envisioned automated and semi-automated checking systems and recommended policy changes across the media ecosystem. In this tutorial, we analyze the current state of the Fake News problem, its variations, and summarize the recent research findings on this emerging and timely topic. Specifically, we focus on the efforts from the Artificial Intelligence (AI) and Database (DB) communities to detect fake news and curb its spread and show their strengths and limitations. Finally, we present a set of research questions and suggest how they can be tackled using the combination of both AI and DB approaches.
University of MississippiNaeemul Hassan is an assistant professor in the computer and information science department at the University of Mississippi. He has interest in research areas related to Database, Data Mining, and Natural Language Processing. He received a Ph.D. in Computer Science from the University of Texas at Arlington.
The Pennsylvania State UniversityDongwon Lee is an associate professor in the College of Information Sciences and Technology (a.k.a. iSchool) of The Pennsylvania State University, USA. He researches broadly in Data Science, in particular, on the management of and mining in data in diverse forms including structured records, text, multimedia, social media, and Web. He is also interested in applying the human computation framework to solve data science problems, and detecting/curbing challenging online frauds using machine learning techniques.
SA3: Modelling Planning Domains
Roman Barták, Lukas Chrpa
Research efforts in the Automated Planning community predominantly focus on developing novel planning techniques and incorporating and/or combining them into domain-independent planning engines that can be exploited in a wide range of real-world applications (e.g., Space Exploration, Manufacturing, Urban Traffic Control). In contrast to domain-dependent approaches, where one has to develop an algorithm for solving planning problems in a specific domain, domain-independent approach provides a lot of flexibility by decoupling domain models and planning engines. For being able to exploit domain-independent planning engines, one has to develop a planning domain model which, roughly speaking, describes the environment and actions.
This tutorial focuses on audience from various areas of AI, who is interested in using of domain-independent Automated Planning engines in their research efforts. With regards to the domain modelling process, we will introduce available “machinery”, i.e., languages and knowledge engineering tools, that can be exploited, a “walk-through” of the process, and our practical experience with developing domain models for real-world applications. Attendees will get a basic understanding of the domain modelling process, tools they can exploit, and challenges they will face. A basic level of knowledge of Automated Planning is recommended (on the level of an undergraduate AI course).
Charles University in PragueRoman Barták works as a full professor and a researcher at Charles University in Prague (Czech Republic), where he leads Constraint Satisfaction and Optimization Research Group. His research work focuses on techniques of constraint satisfaction and modeling and their application to planning, scheduling, and other areas.
Czech Technical UniversityLukáš Chrpa is an assistant professor at Artificial Intelligence Center at Czech Technical University in Prague and a part-time researcher at Charles University in Prague. His research concerns intelligent decision-making, applications of domain-independent planning, and using knowledge engineering techniques in designing and developing planning domain models.
SA4: Rulelog: Highly Expressive Semantic Rules with Scalable Deep Reasoning Networks
Benjamin Grosof, Michael Kifer, Paul Fodor, Janine Bloomfield
We cover the fundamental concepts, key technologies, emerging applications, recent progress, and outstanding research issues in Rulelog, a leading approach to deep, fully semantic, logical/probabilistic knowledge representation and reasoning (KRR) for AI and cognitive computing. Rulelog combines tightly with natural language processing (NLP) to both interpret and generate English, and complements machine learning (ML). It interoperates and composes well with graph databases, relational databases, spreadsheets, XML, and expressively simpler rule/ontology systems – and can orchestrate overall hybrid KRR. Developed over the last 25 years, Rulelog is much more feature-full than the previous state-of-the-art practical KRR approaches, yet is computationally affordable. It has capable efficient implementations including Ergo from Coherent Knowledge (free for academic use), and a large subset is in draft as an industry standard. Rulelog extends Datalog (database logic) with higher-order/meta syntax, flexible defeasibility and probabilistic uncertainty, general classical-logic-like formulas (including existentials and disjunctions), and restraint bounded rationality that ensures worst-case polynomial time for query answering. We illustrate Rulelog’s wide applications for deep reasoning and representing complex knowledge – such as policies, regulations/contracts, science, and terminology mappings – including in financial services, accounting, health care, education, privacy, and e-commerce.
Background assumed of participants is only the basics of first-order-logic and relational databases.
AccentureBenjamin Grosof is a Principal Director and Research Fellow in AI at Accenture, is an industry leader in AI knowledge representation, reasoning, and acquisition. He was formerly IBM Research scientist, MIT Sloan professor, RuleML co-founder, senior program manager at the Allen Institute for AI’s predecessor, and co-founder/CEO of Coherent Knowledge.
Stony Brook UniversityMichael Kifer is a Stony Brook University computer science Professor and co-founder/CTO of Coherent Knowledge, a semantic KRR technology startup. He co-invented F-logic, HiLog, and Transaction Logic, among the most widely cited works in computer science, with three prestigious “Test of Time” awards in database management and logic programming.
Stony Brook UniversityPaul Fodor is a Research Assistant Professor in computer science at Stony Brook University, co-founder/Senior Engineer at Coherent Knowledge, and former member of the IBM Watson Jeopardy! Research team, with over 10 years experience in AI KRR, databases research, natural language processing, and stream processing systems.
Coherent KnowledgeJanine Bloomfield is co-founder/COO of Coherent Knowledge with over 4 years experience in developing Rulelog applications and tutorial materials. A Yale University PhD in ecosystems ecology, she was formerly senior scientist, on global climate change, at Environmental Defense Fund doing science communications at national and international level.
SA5: Scalable Deep Learning using CNTK
Yuqing Tang, Sayan Pathak
Deep learning is rapidly transforming the landscape of Artificial Intelligence opening up a new era of possibilities in applications. This tutorial will introduce the Microsoft Cognitive Toolkit (CNTK) — a cutting-edge open-source deep-learning toolkit for Windows and Linux. CNTK is a powerful computation-graph based toolkit for training deep neural networks and inference. Microsoft product groups use CNTK, for example to create the Cortana speech models and web ranking. CNTK supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads, also in combination. Popular network types (e.g. convolution, LSTM and etc.) are supported either natively or can be implemented using CNTK operators, layers and user-defined lambda functions. CNTK scales to multiple GPU servers and is designed around efficiency. This tutorial will cover basic deep learning models, such as feed-forward networks, convolution, recurrence. We will also discuss the GAN (Generative Adversarial Network) and deep reinforcement learning. In addition, we will dive deep into the underlying CNTK specific training techniques for scalable training over large data sets, such as 1-bit SGD, variable length sequence packing, parallel and distributed per-sample training. Microsoft Azure cloud instances will be provided for the tutorial audiences during the course to practice deep networks programming in CNTK.
Microsoft AI & ResearchYuqing Tang is an Applied Scientist II at Microsoft AI & Research. He is a member of the Microsoft deep learning CNTK toolkit team. Before joining Microsoft, he was at the Robotics Institute at Carnegie Mellon University first in 2012 as a postdoctoral fellow and later in 2015 as a project scientist. He has published more than 40 technical papers in Artificial Intelligence.
Cognitive ToolkitSayan Pathak, Ph.D., is a Principal Machine Learning Scientist in Cognitive Toolkit (formerly CNTK) team at Microsoft. He has published and commercialized cutting edge computer vision and machine learning technology to big data problems applied to medical imaging, neuroscience, computational advertising and social network domains. Prior to joining Microsoft, he worked at Allen Institute for Brain Science. He has been a consultant to several startups and principal investigator on several US National Institutes of Health (NIH) grants. He is a faculty at the University of Washington for 15 years and is Affiliate Professor in CSE at the Indian Institute of Technology, Kharagpur, India for over 4 years. He has taught several courses (namely Image Computing Systems, Information Retrieval, Social Networks, Machine Learning) at the undergraduate and graduate level. He has served in committees of several doctoral and masters students
SA6: Strategic, Online Learning, and Computational Aspects of Social Network Science
Ramasuri Narayanam, Yadati Narahari
This tutorial provides the conceptual underpinnings of the use of game theoretic models as well as online multi-agent learning models in social network analysis and brings out how these models supplement and complement existing approaches for social network analysis. In the first part of the tutorial, we provide rigorous foundations of relevant concepts in game theory, mechanism design, network science, and online learning in multi-agent network systems. In the second part of the tutorial, we bring out how game theoretic approach and online multi-agent learning approach help analyze key problems in network science better and also how to apply these technical concepts to problem solving in a rigorous way. In particular, we present a comprehensive study of a few contemporary and pertinent problems in social networks such as social network formation games, social network monetization, design of incentive mechanisms, and economics of networks.
At the end of the tutorial, we expect the attendees understand the following technical aspects: (a) Foundational concepts in network science, game theory and mechanism design, (b) Need for a game theoretic approach to deal with strategic and economic aspects of social networks, (c) Online learning in multi-agent network systems (including the settings wherein the agents exhibit strategic behavior), and (d) Design of computationally efficient algorithms for various important problems in network science.
IBM Research – IndiaRamasuri Narayanam works as Research Scientist at IBM Research – India since December 2010. Prior to this, he received Ph.D. degree from Indian Institute of Science, Bangalore, India. His research interests are Social Media Analytics, Business Analytics, Game Theory, Graph Data Mining, Multi-Agent Decision Making, Machine Learning.
Indian Institute of Science, BangaloreProf Y. Narahari is a Professor at Indian Institute of Science, Bangalore, India. The focus of his current research is to apply game theory and mechanism-design to research problems at the interface of computer science and economics. In particular, he is interested in algorithmic game theory, dynamic mechanisms with learning, crowd-sourcing, and online education.
SA7Q: Recent Advances in Structured Prediction
Jana Doppa, Liping Liu, Chao Ma
Structured Prediction (SP) is an exciting field with a number of potential applications including natural language processing, computer vision, planning, and bioinformatics. There are several advances in the structured prediction literature including new frameworks, algorithms, theory, and analysis. Our tutorial will present a unifying view of all the existing frameworks for solving structured prediction problems; cover recent advances (e.g., search-based structured prediction, amortized inference, PAC theory for inference, multi-task structured prediction, and integrating deep learning techniques with structured prediction frameworks); and point to fertile areas of research from both technical and application point of view.
Washington State University, PullmanJana Doppa is an Assistant Professor of Computer Science at Washington State University, Pullman. He earned his PhD working with the Artificial Intelligence (AI) group at Oregon State University (2014); and his MTech from Indian Institute of Technology (IIT), Kanpur, India (2006). His general research interests are in the broad field of AI and its applications including planning, natural language processing, computer vision, electronic design automation, and databases. He received a Outstanding Paper Award for his structured prediction work at the AAAI (2013) conference and a Google Faculty Research Award (2015). His PhD dissertation entitled “Integrating Learning and Search for Structured Prediction” was nominated for the ACM Doctoral Dissertation Award (2015). He is a editorial board member of the Journal of Artificial Intelligence Research, and regularly serves on the Program Committee of top-tier conferences including AAAI, IJCAI, ICML, NIPS, AISTATS, ICAPS, and KDD. He taught a tutorial on Structured Prediction at IJCAI-2016 conference, and co-taught tutorials on Energy-Efficient and Reliable 3D Manycore Systems at ICCAD-2016 conference, “Data Analytics enables Energy-Efficiency and Robustness: From Mobile to Manycores, Datacenters, and Networks” and “Adaptive Manycore Architectures for Big Data Computing” at Embedded Systems Week-2017 conference.
Tufts UniversityLiping Liu is an Assistant Professor of Computer Science at Tufts University. He earned his PhD at Oregon State University, held a post-doctoral associate position at Columbia University working with David Blei, and also worked on commercial data analysis at IBM T.J. Watson Research Lab. His research interests include probabilistic modeling, classification, and Bayesian deep learning within machine learning. He also applies these machine learning techniques to ecology studies. He served as a reviewer and PC member for several machine learning conferences and journals.
Oregon State UniversityChao Ma is a sixth year PhD student with the Artificial Intelligence group at Oregon State University. His general research interests are in Artificial Intelligence and Machine Learning with applications to natural language processing. His PhD thesis focuses of developing computationally-efficient learning and inference techniques to solve multi-task structured prediction problems. He has developed several search-based learning algorithms and published multiple papers at AI and NLP conferences.
SA9Q: Responsible Artificial Intelligence
The development, application, and capabilities of AI-based systems are evolving rapidly, leaving largely unanswered a broad range of important short- and long-term questions related to the social impact, governance, and ethical implementations of these technologies and practices. The ultimate success of AI, as well as the future wellbeing of humankind, demand current research to take these issues into consideration, not only as a ‘checklist’ for results but as a foundation for the whole research field.
Developments in autonomy and learning techniques are rapidly enabling AI systems to decide and act without direct human control. Greater autonomy must come with greater responsibility. Social interactions with AI systems require trust. Trust on machines must be based on transparency, since humans cannot relate to the ways of doings of machines. Algorithm development has so far been led by the goal of improving performance, leading to opaque black boxes. Putting human values at the core of AI systems calls for a mind-shift of researchers and developers towards the goal of improving transparency rather than performance, which will lead to novel and exciting algorithms. Increasingly we expect systems to be accountable for and explain their decisions. The following questions will be discussed:
- What does ethics and responsibility mean wrt artificial systems?
- Can we program systems to be ethical?
- And, should we want to have systems that are programmed to be ethical?
- What is our responsibility as researchers?
In this tutorial, we will discuss the principles of Accountability, Responsibility and Transparency (ART) in Artificial Intelligence and provide pointers to legal and ethical theories as well as discuss possible avenues for implementation.
TUVirginia Dignum is Associate Professor on Social Artificial Intelligence at TU. Her research focuses on value-sensitive design of intelligent systems, in particular on the formalisation of ethical and normative behaviours and social interactions. She is Executive Director of the Delft Design for Values Institute, Executive Committee member of IEEE Initiative on Ethics of Autonomous Systems and Director the AI and Robotics MSc program at TU Delft.
SA10Q: Multi-Agent Distributed Constrained Optimization
Ferdinando Fioretto, William Yeoh, Roie Zivan
Teams of agents often have to coordinate their decisions in a distributed manner to achieve both individual and shared goals. Example applications include service-oriented computing, sensor networks, and coordination of smart devices in smart homes. The Distributed Constraint Optimization Problem (DCOP) formulation has emerged as a popular way to model such problems, where the agents coordinate with each other to take on values so as to minimize the sum of the resulting constraint costs, which are dependent on the values of the agents.
In this tutorial, we will provide an overview of the DCOP model and present an accessible and structured overview of the available algorithmic approaches to solve DCOPs. We will also discuss recent extensions to the DCOP framework, such as agents acting in dynamic environments. Finally, we will discuss the suitable applications that can be modeled and solved as DCOPs, and conclude with the most recurrent challenges and open questions.
The target audience is the broad AI community (including researchers in multiagent systems). General knowledge of artificial intelligence concepts such as search and optimization is presumed.
University of MichiganFerdinando Fioretto is a postdoctoral researcher at the University of Michigan. His research focuses on multiagent systems, data privacy, and discrete optimization. His dissertation was awarded “the best AI dissertation” from the Italian Association of Artificial Intelligence in 2017.
Washington University, St. LouisWilliam Yeoh is an assistant professor in the Computer Science and Engineering Department at Washington University in St. Louis. His research interests include multi-agent systems, distributed constraint reasoning, and planning with uncertainty. He is an NSF CAREER awardee and was named in IEEE’s 2015 AI’s 10-to-Watch list.
Ben Gurion UniversityRoie Zivan is a senior lecturer (assistant professor) in the Industrial Engineering and Management Department at Ben Gurion University of the Negev. His research interests include multi-agent systems and distributed constraint reasoning. He won the best paper award at CP 2005.
SP1: Artificial Intelligence and Games
In recent years, the use of video games both as application domain and benchmarks for artificial intelligence has seen rapid progress and a sharp rise in popularity. Compared to board games, video games offer a range of new challenges, from spatial navigation in continuous state- and action-spaces to long-term dependencies, real-time constraints and in some cases visual inputs from complex graphics. This tutorial will give an overview of key research challenges and methods of choice in research on AI for games. I will discuss the various ways in which AI methods can be used in video games (e.g. to play the game, model players, generate content, adapt the game) and the ways in which video games can be used as benchmarks for AI methods. The audience is the AI community in general, or more precisely those within the community that are interested in either doing research of use to the video games industry or to use video games as testbeds or benchmarks.
New York UniversityJulian Togelius is an associate professor at New York University. He holds a BA from Lund University and a PhD from the University of Essex, and is the Editor-in-Chief of IEEE Transactions on Games. His main research interests are AI for games and games for AI.
SP2: Cognitive Robotics in Industrial Settings Competition
William Harrison III, Erez Karpas, Zeid Kootbally, Tim Niemueller, Craig Schlenoff, Eric Timmons, Tiago Vaquero
This tutorial will describe two similar competitions involving Cognitive Robotics in Industrial Settings. The first competition is the Planning and Execution Competition for Logistics Robots (PEXCLR), based on the real-world RoboCup Logistics League (RCLL). This competition deals with a multi-robot system, and requires a group of robots to maintain and optimize a production flow in a virtual factory according to a dynamic order schedule. The second competition is the Agile Robotics for Industrial Automation Competition (ARIAC), which focuses on the task of product picking and kit building. The tasks of both competitions have a high potential for automation and can benefit from planning and robust execution to deal with dynamic orders and execution failures.
In the tutorial we give a brief introduction to the competition scenarios, followed by details about the
technical infrastructure. Emphasis will be put on the available interfaces and how to interact with the simulation. A hands-on session will allow to work with some pre-existing agents and modify them to get a feel for the simplicity of the interfaces. We will close with an outlook on envisioned changes for the upcoming competition.
National Institute of Standards and TechnologyWilliam Harrison is a Research Engineer working on the Agility Performance of Robotic Systems project at the National Institute of Standards and Technology. His research interests include industrial robotic simulation and augmented and virtual reality.
Technion – Israel Institute of TechnologyDr. Erez Karpas is a senior lecturer at the Faculty of Industrial Engineering and Management, Technion – Israel Institute of Technology. His main research interests are artificial intelligence and robotics. Prior to that he was a postdoctoral associate at the Model-based Embedded and Robotics Systems Group at MIT, under the supervision of Prof. Brian Williams. and before that, a research fellow and the research coordinator of the Technion-Microsoft Electronic-Commerce Research Center, under Prof. Moshe Tennenholtz. He completed his Ph.D. under the supervision of Prof. Carmel Domshlak and Prof. Shaul Markovitch at the Faculty of Industrial Engineering and Management at the Technion – Israel Institute of Technology.
University of Southern California, LAZeid Kootbally is currently a Senior Research Associate with the Department of Aerospace and Mechanical Engineering at the University of Southern California, LA, USA. His research interests are in automated robotic systems. He is specifically interested in knowledge representation and task planning for agile industrial robotics.
RWTH Aachen UniversityTim Niemueller is currently finishing his Ph.D. focusing on task-level reasoning and memory, and knowledge-based system integration for personal and industrial autonomous mobile robots. He has worked for CMU and SRI, and is member of the RoboCup Executive Committee. He led the Carologistics team in the RoboCup Logistics League.
National Institute of Standards and TechnologyCraig Schlenoff is the Associate Program Manager of the Robotic Systems for Smart Manufacturing Program, and the Project Leader of the Agility Performance of Robotic Systems project at the National Institute of Standards and Technology. His research interests include knowledge representation/ontologies and performance evaluation of autonomous systems and industrial robotics.
California Institute of TechnologyTiago Vaquero is a Technical Group Leader in the Artificial Intelligence Group, Planning and Execution Section, of the Jet Propulsion Laboratory, California Institute of Technology. Tiago holds a B.Sc., M.Sc., and Ph.D. from the University of Sao Paulo, Brazil. Tiago previously held a research position at the University of Toronto where he worked on Robotics for Healthcare and Autonomous Telepresence Robots where he worked with Professor Chris Beck. Tiago also held a joint Caltech/MIT and MIT research position where he worked on Resilient Spacecraft Systems and Risk Sensitive Planning/Scheduling algorithms with Professor Brian Williams. At MIT, Tiago also worked on Risk Sensitive Planners and Executives for Autonomous Underwater Vehicles and Autonomous Cars. His research interests include autonomous vehicles, automated planning and scheduling, probabilistic planning, robotic space and underwater exploration, knowledge engineering, artificial intelligence and robotics in general.
SP3: Integrating Learning into Reasoning
Brendan Juba, Loizos Michael
Machine learning is very successful at capturing knowledge that is difficult to express by hand. We naturally therefore wish to use machine learning to produce knowledge bases, but the tentative nature of such knowledge requires a semantics that is less strict than classical logics. To capture such knowledge, we will introduce PAC-Semantics. In this formal framework, we will demonstrate quantifiable benefits to integrating learning and reasoning processes, and illustrate some simple techniques for obtaining such benefits. We will also see some new perspectives that PAC-Semantics and these integrated learning and reasoning methods offer on classical issues such as nonmonotonic reasoning, the qualification problem, elaboration tolerance, and abductive reasoning. We will also highlight some issues that have not been addressed, and still have substantial scope for interesting work in Knowledge Representation and Reasoning. We assume knowledge of basic logic and propositional proof systems such as resolution and chaining. We will only assume very elementary knowledge of probability.
Washington University, St. LouisBrendan Juba is an assistant professor of computer science at Washington University in St. Louis. His research interests include learning theory, reasoning algorithms, and theoretical computer science more broadly. He authored “Universal Semantic Communication,” concerning communication without a common language in 2011. He received a 2015 AFOSR Young Investigator Award.
Open University of CyprusLoizos Michael is an Assistant Professor at Open University of Cyprus. He founded and directs the Computational Cognition Lab, and he leads the cross-institutional M.Sc. Program in Cognitive Systems. His main research interests lie at the intersection of computational learning theory, logic-based knowledge representation, and commonsense reasoning.
SP4: Knowledge Graph Construction from Web Corpora
Mayank Kejriwal, Craig Knoblock, Pedro Szekely
Knowledge Graphs (KGs) like Wikidata, NELL and DBPedia have recently played instrumental roles in several machine learning applications, including search and information retrieval, information extraction, and data mining. Constructing knowledge graphs is a difficult problem typically studied for natural language documents. With the rapid rise in Web data, there are interesting opportunities to construct domain-specific knowledge graphs over corpora that have been crawled or acquired through techniques like focused crawling. In this tutorial, we survey the techniques for knowledge graph construction from domain-specific Web corpora. Topics that we cover include both Web-focused technology like state-of-the-art wrappers, standard information extractors like conditional random fields, NLP rules and glossaries, as well as more advanced techniques for cleaning the constructed KG, such as probabilistic soft logic and knowledge graph embeddings. This tutorial will have hands-on components. This tutorial is designed for a general AI practitioner, whether from research or industry. Participants will only be expected to have basic knowledge of machine learning. Some experience with Python and UNIX-style commands will also be useful.
USC Information Sciences Institute (ISI)Mayank Kejriwal is a research scientist at the USC Information Sciences Institute (ISI). Prior to joining ISI, he graduated in 2016 with his Ph.D. from the University of Texas at Austin. At ISI, he works extensively on problems of knowledge graph construction and information extraction from Web corpora.
USCCraig Knoblock is a Research Professor of both Computer Science and Spatial Sciences at USC. He has decades of teaching experience in information integration, and has more than 250 peer-reviewed publications on topics relevant to knowledge graph construction, including source modeling, entity resolution, data cleaning, and Web information extraction.
USCPedro Szekely is a research lead at ISI and a research associate professor at USC. His research focuses on the rapid construction of domain-specific knowledge graphs and integration of open source data from the Web. He has won multiple best paper awards, and has decades of teaching experience.
SP5: Machine Reading for Precision Medicine
Hoifung Poon, Chris Quirk, Scott Wen-Tau Yih
Medicine today is imprecise. For the top 20 drugs by gross sales, 80% of patients are non-responders. By estimation, one third of US healthcare spending does not lead to improvement in patient’s health condition, amounting to a staggering trillion-dollar waste each year. The advent of big data promises to revolutionize medicine by making it more personalized and effective. Opportunities range from cancer to chronic diseases, from treatment to early detection. But big data also presents a grand challenge of information overload, making it difficult to distill knowledge from data, and separate signal from noise. AI can play a key role in translating big data to optimal medical decisions.
The goal of this tutorial is to introduce AI researchers to precision medicine and showcase the vast opportunities for impact in this burgeoning field with great societal significance. We will introduce machine reading for addressing the knowledge bottleneck in precision medicine, and review fundamental challenges, state-of-the art methods, and important applications. To reduce the entry barrier for AI researchers, we will present available datasets, medical resources, and practical issues. The tutorial will be accessible to anyone with basic knowledge of AI. No background in biology or healthcare is required.
Microsoft ResearchHoifung Poon leads Project Hanover at Microsoft Research to advance machine learning and NLP for precision medicine. His past work spans a wide range of problems in NLP and machine learning, and has been recognized with Best Paper Awards from premier venues such as NAACL, EMNLP, and UAI.
Microsoft ResearchChris Quirk is a Principal Researcher at Microsoft. His research in Natural Language Processing has primarily focused on machine translation, information extraction, and semantic parsing, leading to advances in production technology as well as over 60 academic publications.
Scott Wen-tau Yih
Allen Institute for Artificial IntelligenceScott Wen-tau Yih is a Principal Research Scientist at Allen Institute for Artificial Intelligence (AI2). His main research interest is natural language processing, with recent focus on question answering and semantic parsing. Yih has co-authored more than 70 papers. Awards include a CoNLL-2011 best paper and an ACL-2015 outstanding paper.
SP6: When Advanced Machine Learning Meets Intelligent Recommender Systems
Liang Hu, Longbing Cao, Jian Cao, Songlei Jian
Nowadays, the renaissance of artificial intelligence (AI) has attracted huge attention from every corner of the world. In particular, machine learning approaches have deeply involved in AI research in almost all areas. Recommender systems (RS), as probably one of the most widely used AI systems, has integrated into every part of our daily life. In this AI age, state-of-the-art machine learning approaches, e.g. deep learning, have become the primary choice to model advanced RSs. Current machine learning methods are built on data, therefore the recommendation tasks can be regarded as typical AI problems to learn and infer from data.
The goal of this tutorial aims to enable audience with a comprehensive understanding and relevant techniques of how to apply state-of-the-art machine learning approaches to build more sensible RSs in contexts with various heterogeneous data and complex relations. In this tutorial, we will present a systematic review and applications of recent advanced machine learning techniques to build real-life intelligent RSs. In particular, we will demonstrate how to enhance RSs with Comprehensive, Complementary, and Contextual (3C) information by coupling relevant heterogeneous data. This tutorial will analyze data, challenges, and business needs in advanced recommendation problems, and introduce recent advances in machine learning to model the 3C-based next-generation RSs.
University of Technology Sydney, AustraliaLiang Hu is a Ph.D. candidate with Advanced Analytics Institute, University of Technology Sydney, Australia. His research interests include recommender systems, data mining, machine learning and general artificial intelligence. He has published a number of papers in top-rank international conferences and journals in the area of recommender systems, including WWW, IJCAI, AAAI, ICDM, ICWS, TOIS, JWSR.
University of Technology Sydney (UTS)Longbing Cao is a professor of information technology at the University of Technology Sydney (UTS), Australia. He is the Chair of ACM SIGKDD Australia and New Zealand Chapter, IEEE Task Force on Data Science and Advanced Analytics, and IEEE Task Force on Behavioral, Economic and Socio-cultural Computing. His primary research interests include data science and mining, machine learning, behavior informatics, agent mining, multi-agent systems, and open complex intelligent systems. He is currently dedicated to the research on non-iid learning in big data and behavior informatics which involve very wide enterprise applications.
Shanghai Jiao Tong University (SJTU)Jian Cao is a Professor with the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), China, and the deputy head of the department. He is the director of the SJTU & Morgan Stanley Joint Research Center on Financial Service Innovation. He is also the leader of the Lab for Collaborative Intelligent Technology. His research interests include service computing, recommender systems, artificial intelligence and data analytics.
University of Technology Sydney, Australia and National University of Defense Technology, ChinaSonglei Jian is a Ph.D. candidate with Advanced Analytics Institute, University of Technology Sydney, Australia and National University of Defense Technology, China. Her research interests include representation learning, recommender systems and unsupervised learning approach. She has published a number of papers in top-rank international conferences and journals in these areas.
SP7Q: Recent Advances and Applications in Markov Logic Networks
Deepak Venugopal, Vibhav Gogate, Vincent Ng
The purpose of this tutorial is to provide an overview of recent advances in scalable inference and learning in Markov logic Networks (MLNs) along with recent MLN-based joint inference applications in NLP. The tutorial will be of relevance to both application designers and researchers interested in understanding the theoretical foundations of MLNs as well as practical tricks to make them work well in the real-world. The tutorial will focus on recent lifted inference algorithms for MLNs based on both exact and approximate symmetries. It will also cover scalable approximate weight learning methods for MLNs that utilize these symmetries. Further, we will present practical tools for MLN inference and learning, along with tricks to use these tools to scale up to problems of practical interest. Finally, we will present several NLP applications that use MLNs to perform joint inference to illustrate to the audience, how MLNs can be designed and applied to real-world problems. Elementary probability and statistics are a pre-requisite for this tutorial. Knowledge of Probabilistic Graphical Models (Bayesian networks, Markov networks) will not be assumed but is a plus.
University of MemphisDeepak Venugopal is an assistant professor in computer science at the University of Memphis. His main research interests are in Probabilistic Graphical Models, Statistical Relational Models and their applications. His research work has resulted in several key techniques for scalable inference and learning in Statistical Relational Models.
University of Texas at DallasVibhav Gogate is an Associate Professor in the Computer Science Department at the University of Texas at Dallas. His research interests are in Probabilistic Graphical Models and their first-order extensions. He is best known for his work on lifted inference algorithms combining power of logic and probability.
University of Texas at DallasVincent Ng is a Professor of Computer Science and a member of the Human Language Technology Research Institute at the University of Texas at Dallas. He is best known for his work on coreference resolution in the NLP community. He has recently been modeling complex NLP tasks using Markov Logic.
SP8Q: Implementing Motivation and Emotion in AI Architectures
The tutorial addresses researchers that aim at implementing plausible and non-trivial models of emotion and motivation in AI architectures, or that want to gain a deeper understanding of how to address these topics in the context of computational cognitive models, human computer interaction, affective user modeling, computer games and models of personality.
The audience will gain a conceptual understanding of capturing emotion (differences in the how of cognition) and motivation (the why of cognition), the generation of autonomous goals, motivation-driven decision-making and the regulation of action. This understanding can be applied in many practical and theoretical contexts, especially for the implementation of concrete agent architectures.
Motivation can be modeled as a set of mechanisms that are directed on the regulation of needs of an agent, specifically the selection, creation and execution of behaviors to reach situations that afford the satisfaction and avoid the frustration of needs. Human behavior is directed by specific physiological, social and cognitive needs. Much of the variation in human personality can be understood as individual differences in the parameterization of these needs. Conversely, affective states can be modeled as the configuration of cognition, via a set of modulators that capture valence, arousal, dominance/submission and the direction, focus and depth of attention. The tutorial will address these factors in detail and give suggestions for implementation in computational models.
Harvard Program for Evolutionary Dynamics in Cambridge, MassachusettsJoscha Bach, Ph.D. is a cognitive scientist who worked and published about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems. He earned his Ph.D. in cognitive science from the University of Osnabrück, Germany. He is especially interested in the philosophy of AI, and in using computational models and conceptual tools to understand our minds and what makes us human. Joscha has taught computer science, AI, and cognitive science at the Humboldt-University of Berlin, the Institute for Cognitive Science at Osnabrück, and the MIT Media Lab, and authored the book “Principles of Synthetic Intelligence” (Oxford University Press). He currently works at the Harvard Program for Evolutionary Dynamics in Cambridge, Massachusetts.
SP9Q: AI Techniques for Price Prediction in Commodity Markets
Ramasuri Narayanam, Rohith Vallam, Ritwik Chaudhuri, Manish Kataria, Gyana Parija, Fatemeh Jahedpari
In this tutorial, we wish to cover the foundational, methodological, and system development aspects of Price Prediction of certain raw materials (such as Ethylene, Hydrocorbons and Methyl Methacrylate) that are volatile and not traded online in Commodity markets. There exist diverse and evolving information sources which can potentially influence the prices of raw materials in commodity markets. Multiple machine learning based methodologies — such as non-linear regression, random forest, and expert-based learning — are present in the state-of-the-art to predict the price of raw materials. In these machine learning based methods, the knowledge extracted from each of the heterogeneous data sources on the price predictions is aggregated in some meaningful manner. However, in reality, these diverse information sources are correlated and any methodology that derives the price predictions by working with these diverse information sources simultaneously is more effective as there won’t be any loss of information. Hence, if there is a way for these experts holding different information sources to collaborate on the price prediction task, then more effective predictions can be produced compared to the standard machine learning models. And prediction market based approach is exactly suitable to this sort of requirement that enable interaction and collaboration among multiple independent experts.
Motivated by this, this tutorial provides the conceptual underpinnings of the use of prediction markets for the task of predicting price for raw materials in commodity markets. To the best of our knowledge, it is first attempt to build and implement multi-agent based prediction markets for price prediction in commodity markets. The prediction market based approach outperforms the machine learning based predictions as the participating agents in the prediction market evolve with time in terms of their knowledge and thus leading better price predictions. Broadly this topic belongs to the area of collaborative decision making in the area of multi-agent systems in Artificial Intelligence. The approach presented in this tutorial brings out how these models supplement and complement existing machine learning based approaches for price prediction. In the first part of the tutorial, we provide rigorous foundations of relevant concepts in prediction markets, multi-agent based learning, and domain of the commodity markets. In the second part of the tutorial, we bring out the design and analysis of a multi-agent based prediction market. We also show a Demo that is built using AnyLogic Simulation Software to demonstrate the performance of the proposed prediction market based approach.
IBM Research – IndiaRamasuri Narayanam works as Research Scientist at IBM Research – India since December 2010. Prior to this, he received Ph.D. degree from Indian Institute of Science, Bangalore, India. His research interests are Social Media Analytics, Business Analytics, Game Theory, Graph Data Mining, Multi-Agent Decision Making, Machine Learning.
Rohith D. Vallam
IBM Research – IndiaRohith D. Vallam is a Research Scientist at IBM Research – India. Prior to that, he received his Ph.D. degree from Indian Institute of Science, Bangalore, India. His research interests are game theory, mechanism design, and machine learning in web-based applications like online education, crowdsourcing, social networks and online auctions.
IBM Research – IndiaRitwik Chaudhuri is a Research Scientist at IBM Research – India. Before that, he received his doctoral degree in Statistics and Operations Research from University of North Carolina, USA, 2014. His research interests are Inverse problems, scaling phenomenon of heavy tails, extreme value theory, stable and semi-stable distributions, machine learning, algorithms and complexity, MCMC.
IBM Research – IndiaManish Kataria is working as Senior Development Researcher at IBM Research – India. His research interests are Business analytics and optimization, collaborative cognition, software architecture. Prior to joining IBM Research, He was involved in developing Enterprise Social Software for close to 9 years. Manish has been a prolific inventor as well.
Gyana R. Parija
IBM Research – IndiaGyana R. Parija is a Senior Technical Staff Member at IBM Research – India. He joined IBM in 1994 after obtaining a doctorate in Industrial Engineering (with a specialization in Operations Research) from the Texas A&M University, USA. His research Interests are Business analytics and optimization, integer programming and stochastic integer programming, production capacity planning, asset-liability management, smarter workforce, collaborative cognition.
University of Wollongong, DubaiFatemeh Jahedpari is a Lecturer at Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, UAE since Sep 2016. Before this, she obtained her doctoral degree from University of Bath, UK. Her research interests are Artificial Intelligence, Machine Learning, Multi-agent Systems, Collective Intelligence, Artificial prediction markets.
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