Workshops

• Eunika Mercier-Laurent
• Waltraut Ritter
• Mieczyslaw L. Owoc

The 7th workshop Artificial Intelligence for Knowledge Management focus on AI applied to face the current challenge such as climate change, eco-innovation, societal innovation and global security. The objective of this multidisciplinary session is to gather both researchers and practitioners to discuss methodological, technical and organizational aspects of AI used for knowledge management and to share the feedback on KM applications using AI. Knowledge management powered by AI for Business Intelligence, advisors, simulators, virtual training, all applications of machine learning to support innovation and eco-innovation, knowledge visualization for improving the creativity and human-machine interfaces, image mining making links between data and images ex bio-detection and others are welcome.

Coming soon

Despite achieving great success in a range of important applications deep learning continues to face challenging questions around its robustness, extrapolation and transfer learning, reasoning and explanation capabilities. Developments in the field of neural-symbolic integration offer an opportunity to address such challenges through the integration of well-founded symbolic Artificial Intelligence (AI) with efficient neural computation. The Workshop on Neural-Symbolic Learning and Reasoning will provide a forum for the presentation, exchange of ideas, and discussion of the key topics related to neural-symbolic computing and AI.

• Hsin-Hsi, Chen, Department of Computer Science and Information Engineering, National Taiwan University & Most Joint Research Center for AI Technology and All Vista Healthcare, Taiwan
• Hiroya, Takamura, Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Japan
• Hen-Hsen, Huang, Department of Computer Science, National Chengchi University, Taiwan
• Chung-Chi, Chen, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan

The aim of this workshop is to provide a forum where international participants can share knowledge on applying NLP to the Financial Technology (FinTech) domain. In order to bridge the gap between the NLP researches and the financial applications, we plan to organize, FinNLP, a workshop on FinTech and NLP. With the sharing of the researchers in FinNLP, the challenging problems of blending FinTech and NLP will be identified, and the future research direction will be shaped.

Coming soon

The 3rd IJCAI Workshop on Affective Computing brings together researchers on affective computing topics, including machine learning for affect recognition from vision, video. wearables, IoT devices, speech and cognitive services, text, and other affective content. The intended audience shall consist of artificial intelligence researchers from core areas such as pattern recognition, probabilistic reasoning, ontologies, learning representation (including deep learning), as well as transdisciplinary and multidisciplinary domains such as data science, spatiotemporal analytics of affect, data modeling and mining, cyber-physical systems (CPS), fog/edge computing, and virtual reality (VR) / augmented reality (AR) / mixed reality systems.

• Atlas Wang
• Yang Shen
• Shuai Huang
• Jiayu Zhou

The BOOM workshop aims at catalyzing synergies among biomedical informatics, artificial intelligence, machine learning, and optimization. This workshop is targeting an audience of applied mathematicians, computer scientists, industrial engineers, bioinformaticians, computational biologists, clinicians and healthcare researchers who are interested in exploring the emerging and fascinating interdisciplinary topics.

• Xing Su, Beijing University of Technology, China
• Yan Kong, The University of Information, Science & Technology, China
• Weihua Li, Auckland University of Technology, New Zealand

The modelling and simulation of complex systems, such as ecosystem, social networks, economic systems and transportation systems, is difficult to accomplish using traditional computational approaches due to their distributed and dynamic features. SSMCS aims to provide a forum for researchers to discuss the use of novel Artificial Intelligence technologies to the modeling and simulation of complex systems, as well as the latest scientific efforts in this field such as the platforms and/or the software tools for smart modeling and simulating.

• Diedrich Wolter, University of Bamberg, Germany
• Matthew Klenk Palo Alto Research Center

The Qualitative Reasoning (QR) community is involved with the development and application of qualitative representations to understand the world from incomplete, imprecise, or uncertain data. QR reaches out to researcher seeking to understand, develop, and exploit the ability to reason qualitatively. This broadly includes: Developing new formalisms and algorithms for qualitative reasoning; building and evaluating predictive, prescriptive, diagnostic, or explanatory qualitative models in novel domains; characterizing how humans learn and reason qualitatively about the (physical) world with incomplete knowledge; developing novel, formal representations to describe central aspects of our world: time, space, change, uncertainty, causality, and continuity.

• Felipe Leno da, Silva, University of São Paulo (Brazil)
• Ruben, Glatt, University of São Paulo (Brazil)
• Patrick, MacAlpine, Microsoft Research (USA)
• Denis, Steckelmacher, Vrije Universiteit Brussel (Belgium)

Reinforcement Learning (RL) has achieved many successes over the years in training autonomous agents to perform simple tasks. However, one of the major remaining challenges in RL is scaling it to high-dimensional, real-world applications. This workshop encourages the discussion of diverse approaches to accelerate, generalize and increase the sample-efficiency and applicability of RL, such as the use of approximations, abstractions, hierarchical approaches, and Transfer Learning.

Coming soon

Maintaining business security is essential for successful businesses and has become one of the biggest challenges in the business development. Typical business security issues include: content-based security, anomalous behavior detection, security in social networks and online ecommerce etc. In recent years, there have seen a dramatic increase in applications of artificial intelligence and data mining techniques to solve business security problems. However, this creates some new security challenges. The new artificial intelligent system itself may become a vulnerable and lucrative target to attackers. The AIBS 2019 workshop aims at providing a venue for presenting and discussing new developments to make the business more secure using AI techniques even when adversary adapts.

• Jose Camacho-Collados, Cardiff University, Cardiff, United Kingdom
• Thierry Declerck, German Research Centre for Artificial Intelligence (DFKI GmbH), Saarbrücken, Germany
• Luis Espinosa-Anke, Cardiff University, Cardiff, United Kingdom
• Dagmar Gromann, Technical University Dresden (TU Dresden), Dresden, Germany
• Mohammad Taher Pilehvar, Iran University of Science and Technology, Iran

SemDeep addresses the open research question of how Semantic Web technologies can be united with state-of-the-art machine learning approaches, e.g., applying theorem provers and logic rules to help explain the inner workings of neural networks, or combining ontological knowledge with data-driven representations like word embeddings in downstream tasks. Following the past editions of this workshop, SempDeep-5 seeks to provide an invigorating environment for discussions on semantically challenging problems that appeal to the Semantic Web and machine learning research and industrial communities. This edition will also feature a challenge on evaluating context-sensitive word meaning representations, which addresses a crucial problem at the intersection of both fields.

• Xin Huang, Hong Kong Baptist University, Hong Kong
• Di Jin, Tianjing University, China
• Yunwen Lei, Southern University of Science and Technology, China
• Jing Liu, Xidian University, China

Incremental social media data of online social networks, microblogs, multi-media sites, and user review sites are imposing new challenges for efficient data management and analysis. The BSMDMA workshop aims to provide a forum for presenting the most recent advances in most recent advances in data management and mining on online social media, related to web search and information retrieval, social network analysis, visualization and summarization, and network science.

• Sergei Kuznetsov, HSE Moscow, Russia
• Amedeo Napoli, LORIA Nancy, France
• Sebastian Rudolph, TU Dresden, Germany

Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at classification, data analysis and data mining, that can be used in Artificial Intelligence (AI) for many purposes, especially for knowledge and data processing. The objective of the workshop is to investigate two main issues: FCA for AI or how can FCA supports various AI activities, e.g. knowledge discovery, knowledge engineering, machine learning, information retrieval and recommendation, and AI within FCA or how can FCA be extended for helping and guiding AI researchers to solve new and complex problems in their domain.

• Gerald Steinbauer, Graz University of Technology, Austria
• Sven Koenig, University of Southern California, USA
• Fredrik Heintz, Linköping University, Sweden
• Tara Chklovski, Iridescent, USA
• Martin Kandlhofer, Graz University of Technology, Austria

In recent years, Artificial Intelligence (AI) has captured the imagination of the public, and become a major topic of discussion. However, Education in AI at the K-12 level is still quite rare. The first International Workshop on Education in Artificial Intelligence K-12 (EduAI) addresses this topic. The main goal of this workshop is to bring together people who are actively involved with and/or interested in K-12 AI education (researchers, teachers, educators, practitioners) and top AI scientists, fostering a mutual exchange of knowledge, ideas and views between those groups. It aims to discuss and find a common ground for how to best implement AI education at the K-12 level.

• Colin de la Higuera, Université de Nantes, UNESCO Chair in teacher training technologies with OER
• Davor Orlic, COO, Knowledge 4 All Foundation
• Maria Fasli, University of Essex, UNESCO Chair in Analytics and Data Science
• John Shawe-Taylor, University College London, UNESCO Chair in Artificial Intelligence

Knowledge 4 All Foundation and Unesco Chairs are organizing a 1-day workshop for experts in Artificial Intelligence addressing scalable solutions or exemplars for the United Nations Sustainable Development Goals, with an emphasis on creating potential sub-networks of researchers around each of the 17 SDGs. The goal of the workshop is to provide a forum for researchers involved in the Unesco SDGs, allowing new researchers to get involved in these essential topics and to increase the visibility of actions already under way. The workshop should act as an important stepping stone towards building an AI roadmap for SDGs, including designing a viable plan for cross-continent cooperation.

• Rafal Rzepka, Hokkaido University
• Jordi Vallverdú, Autonomous University of Barcelona
• Andre Wlodarczyk, Charles de Gaulle University

LACATODA is a multidisciplinary workshop for researchers who develop more sophisticated dialog agents and methods for achieving more natural machine-generated conversation or study problems of human communication which are difficult to mimic algorithmically. LACATODAs gather researchers interested in all aspects of dialog, from knowledge acquisition and sentiment analysis to dialog acts and linguistic phenomena of communication. By combining Natural Language Processing methods with cognitive architectures, machine learning and philosophy of mind, we can discover a new range of intelligent systems that understand us, our environment and our feelings.

• Sheng Li, University of Georgia, USA
• Yaliang Li, Alibaba Group, USA
• Jing Gao, University at Buffalo, USA
• Yun Fu, Northeastern University, USA

Due to the success of deep representation learning, we have observed increasing research efforts in the intersection between vision and language for a better understanding of semantics. Besides, exploiting external semantic knowledge (e.g., semantic relations, knowledge graphs) for vision and text understanding also deserves more attention. This workshop will provide a forum for researchers to review the recent progress of vision and text understanding, with an emphasis on novel approaches that involve a deeper and better semantic understanding of vision and text data.

• Douglas Lange, Naval Information Warfare Center, USA
• Luke Marsh, Defence Science and Technology Group, Australia

Evaluation of systems developed through machine learning in the lab or in the wild after deployment is a critical issue. This workshop will discuss current and proposed science and its application towards ensuring such systems can be validated and certified for use in mission critical situations. We also continue the theme of two previous IJCAI workshops by looking at the implications when these capabilities are used as part of a human-autonomy team.

• Chen Gong
• Weiwei Liu
• Xiaobo Shen
• Joey Tianyi Zhou
• Yew-Soon Ong
• Ivor W. Tsang

This workshop aims to publish state-of-the-art scientific works along the direction of multi-output learning. We welcome all the original submissions with significant novel results, focusing on modelling, algorithm, theory, and real-world applications in this field.

• Qiang Yang，Hong Kong University of Science and Technology
• Yang Liu, Webank
• Fausto Giunchiglia,University of Trento
• Han Yu, Nanyang Technological University
• Shiqiang Wang ,IBM Thomas J. Watson Research Center

The 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML 2019) will focus on machine learning systems with privacy and security. Technical issues include but not limit to data collection, integration, training and modeling, both in the centralized and distributed setting. The workshop intends to provide a forum to discuss the open problems and share the most recent and ground-breaking work on the study and application of GDPR compliant machine learning.

• Takayuki Ito
• Minjie Zhang
• Reyhan Aydogan
• Takanobu Otsuka
• Ahmed Moustafa

Automated Negotiations have been widely studied and are one of the emerging areas of research in the field of Autonomous Agents and Multi-Agent Systems. These days AI systems have been developed by many different companies and organizations. In the near future, if a lot of heterogeneous AI systems are acting in a society, then we do need to have coordination mechanisms based on automated negotiation technologies. It must be complex and also autonomous because of the complexity of our society. ACAN2019 will discuss, among others, the following aspects and topics of such complex automated negotiations within the field of Autonomous Agents and Multi-Agent Systems, which have distinct relationships with IJCAI main conference topics.

• Jeff Dalton, University of Glasgow
• Julia Kiseleva, Microsoft Research & AI
• Aleksandr Chuklin, Google AI
• Mikhail Burtsev, Moscow Institute of Physics and Technology (MIPT)

The Search-Oriented Conversational AI workshop brings together researchers from the Natural Language Processing (NLP), Artificial Intelligence (AI), and Information Retrieval (IR) communities to investigate future directions of research in the area of search-oriented conversational systems. The focus of this installment seeks to broaden participation between research and industry.

• An, Zeng, Guangdong University of Technology
• DaoQiang, Zhang, Nanjing University of Aeronautics and Astronautics
• TianYong, Hao, South China Normal University
• Xiaowei, Song, Simon Fraser University
• Yiyu, Shi, University of Norte Dame
• Dan, Pan, Guangdong Construction Polytechnic

The aim of this workshop called Human Brain and Artificial Intelligence (HBAI) is to bring together active researchers and practitioners in the frontier of Artificial Intelligence (AI) and Human Brain Research for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on computational brain science, brain-inspired technologies and their applications. AI holds a tremendous repertoire of algorithms and methods that constitute the core of different topics of computational brain science. The aim of human brain research is to achieve a comprehensive basic understanding in the field of human brain function that serves as a foundation for future translational research and the development of effective therapies for various neurological diseases. HBAI goals are two folds: How can AI techniques contribute to human brain research? And, how can human brain research raise new fundamental questions in AI? Contributions will clearly point out answers to one of these goals focusing on AI techniques and their applications as well as focusing on human brain problems.

• Xiaoli Li
• Peilin Zhao
• Zhenghua Chen
• Le Zhang

Human activity recognition (HAR) can benefit various applications. Deep learning is an ideal candidate for HAR due to its strong model capacity on big and complex data. However, many challenging research problems in terms of accuracy, device heterogeneous, environment changes, etc. remain unsolved for deep learning based HAR. This workshop aims to prompt state-of-the-art approaches on deep learning for human activity recognition.

• Guodong Long
• Artur W. Dubrawski
• Zachary A. Pardos
• Edgar Kalns

This workshop brings together multidisciplinary researchers to explore a newly released multimodal public dataset that records students’ studying behavior on an adaptive learning platform via multiple channels with various sensors and collecting technique, e.g. EEG-based attention value is collected by using brain-computer-interface devices, students’ facial expressions are collected using webcams. In particular, the workshop aims to draw insights from the community’s analyses of the dataset that will enable us to gain a better understanding of the human learning processes, to develop and evaluate various teaching or learning models, and to inspire the development of new adaptive educational solutions. Furthermore, the workshop will also exploit AI-based multimodal data processing methods to understand big data in an education context and to advance new educational applications fueled with data-driven AI technology.

• Tim Miller
• Rosina Weber
• Daniele Magazenni
• David Aha

As AI becomes more ubiquitous, complex and consequential, the need for people to understand how decisions are made and to judge their correctness becomes increasingly crucial due to concerns of ethics and trust. The field of Explainable AI (XAI), aims to address this problem by designing AI whose decisions can be understood by humans. This workshop brings together researchers working in explainable AI to share and learning about recent research, with the hope of fostering meaningful connections between researchers from diverse backgrounds, including but not limited to artificial intelligence, human-computer interaction, human factors, philosophy, cognitive & social psychology.

• Anika Schumann, IBM Research – Zurich, Switzerland
• Stephan Sigg, Aalto University, Finland
• Sebastian Bader, University of Rostock, Germany

The Internet of Things (IoT) is the internetworking of physical devices with embedded electronics and an internet address that can transfer data without human interaction. Examples are wearable devices, environmental sensors, factory machinery, devices in homes and buildings, or vehicle components. These connected devices produce an exponentially growing amount of data including sensor data in time series format, image, sound and video data. Today, most of this data is unused. This workshop will explore how AI techniques can be used to (i) Make sense of these vast amounts of IoT data, (ii) Reason about connected physical systems and environments, (iii) Assist humans with the execution of actions.

• Pushpak Bhattacharya, IIT Patna, India
• Niranjan Nayak, Microsoft AI, India
• Manoj K Chinnakotla, Bing, USA
• Puneet Agrawal, Microsoft AI, India
• Kedhar Nath Narahari, Microsoft AI, India

In this workshop, we ask the question, what will make AI agents more human-like? What are those aspects, and how can we develop algorithms and techniques to make progress in achieving those aspects. These aspects could be pertaining to both Intelligence Quotient (IQ) as well as Emotional Quotient (EQ). All papers submitted in the workshop are non-archival and we welcome early works.

Coming soon

We have roughly defined the ``Language Sense'' as an expression that underlines an affective or psychological aspects of language. We would like to gather researchers with interests in language technologies that inspire us through emotions, enchants us, engage artistically or aesthetically, etc. In addition, we would like to apply theoretical reasoning approaches such as abduction or induction to the above mentioned affective or psychological aspects of language.

• Patrick De Causmaecker, KU Leuven, Belgium
• Michele Lombardi, University of Bologna, Italy
• Yingqian Zhang, TU Eindhoven, The Netherlands

Data science and optimisation are closely related. Many problems in data science can be solved using optimisers, and optimisation problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Heuristics used in data science as well as in optimisation implicitly make use of characteristics of the data sets or optimization problem instances to be expected; these characteristics can be inferred through data science methods. The aim of the workshop is to organize an open discussion and exchange of ideas by researchers from Data Science and Operations Research/optimisation domains in order to identify how techniques from these two fields can benefit each other.

• Frans Coenen, University of Liverpool UK
• Nirmalie Wiratunga, Robert Gordon University UK
• Sadiq Sani,Robert Gordon University UK
• Zina Ibrahim, King's College London UK
• Jonathan Rubin, Philips Research North America USA
• Honghan Wu, University of Edinburgh(UK
• Anjana Wijekoon, Robert Gordon University UK
• Shehroz Khan, Toronto Rehabilitation Insitute, Canada
• Alex Mihailidis, University of Toronto
• Sebastian Bader, University of Rostock, Germany

The Knowledge Discovery in Healthcare Data (KDH) workshop series was established in 2016 to present AI research efforts to solve pressing problems in healthcare. The workshop series aims to bring together clinical and AI researchers to foster collaborative discussions. The workshop will also features a KDH challenge dataset for multi-modal ML.

The ARIAL workshop is focussed on the development of novel and scalable AI techniques within the realms of Aging, Rehabilitation and Independent Assisted Living (ARIAL). The ARIAL workshop brings together AI researchers, clinicians, practitioners, and engineers to solve important health problems for older adults.

• Huáscar Espinoza, Commissariat à l´Energie Atomique, France
• Han Yu, Nanyang Technological University, Singapore
• Xiaowei Huang, University of Liverpool, UK
• Freddy Lecue, Thales, Canada
• José Hernández-Orallo, Universitat Politècnica de València, Spain
• Seán Ó hÉigeartaigh, University of Cambridge, UK
• Richard Mallah, Future of Life Institute, USA
• Cynthia Chen, University of Hong Kong, China

In the last decade, there has been a growing concern on risks of Artificial Intelligence (AI). Safety is becoming increasingly relevant as humans are progressively ruled out from the decision/control loop of intelligent, and learning-enabled machines. In particular, the technical foundations and assumptions on which traditional safety engineering principles are based, are inadequate for systems in which AI algorithms, in particular Machine Learning (ML), are interacting with the physical world at increasingly higher levels of autonomy. The AISafety workshop seeks to explore new ideas on safety engineering, as well as broader strategic, ethical and policy aspects of safety-critical AI-based systems.

• Nika Haghtalab, Microsoft Research
• Eric Horvitz, Microsoft Research
• Ezinne Nwankwo, Duke University
• Andreas Theodorou, Umeå University
• Bryan Wilder, University of Southern California

This workshop will explore how artificial intelligence can contribute to solving social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can AI initiatives be deployed in an ethical, inclusive, and accountable manner? To address such questions, this workshop will bring together researchers and practitioners across artificial intelligence and a range of application domains. The objective is to share the current state of research and practice, explore directions for future work, and create opportunities for collaboration.

• Liron Cohen, University of Southern California, USA
• Hang Ma, University of Southern California, USA
• Daniel Harabor, Monash University, Australia
• Nathan Sturtevant, University of Alberta, Canada
• Ariel Felner, Ben-Gurion University (Israel)
• Sven Koenig, University of Southern California, USA

The Multi-agent Pathfinding Problem (MAPF) takes as input a team of agents that need to plan collision-free paths from their current locations to their target locations. MAPF has been shown to be NP-hard for several different cost functions, yet one must find high-quality collision-free paths for the agents quickly. Recent years have seen a significant rise of different formulations of the MAPF problem and diverse approaches with varying complexities, properties and applications. This workshop aims to gather the MAPF community in order to present new and ongoing research, discuss issues that pertain to high-quality research and cross-fertilize ideas between different groups.

• Henny Admoni, Carnegie Mellon University
• Tapomayukh Bhattacharjee, University of Washington
• Katerina Fragkiadaki, Carnegie Mellon University
• Oliver Kroemer, Carnegie Mellon University
• Michael Spranger, Sony

The tasks of cooking and eating present exciting research and application challenges for AI systems. Research on food preparation, delivery, and consumption build on universal human experiences and address critical human needs. At the same time, successfully developing AI systems for cooking and eating presents many challenges across the spectrum of AI: knowledge representation, machine learning, planning, robotics, assistive technologies, human-computer and human-robot interaction. The AI&Food workshop will include both industry and academic perspectives, and targets researchers and practitioners from any field of AI with an interest in food and cooking.

• Parisa Kordjamshidi, Tulane University/IHMC
• Hannaneh Hajishirazi, University of Washington
• Quan Guo, Tulane University/Sichuan University
• Nikolaos Vasiloglou II, relationalAI
• Kristian Kersting, TU Darmstad
• Dan Roth, UPenn

This workshop aims to discuss the challenges of programming for designing complex intelligent systems that a) require a number of interacting learning and reasoning components, b) exploit the relational structure of the data, c) exploit declarative and procedural domain knowledge in various forms in (statistical/deep) learning. We aim to explore the ideas related to the representation of the programs that are learnable and express data and knowledge declarations, this is related to the ideas in statistical relational learning, probabilistic (logical) programming, end-to-end differentiable programming but not limited to these, therefore it comes to what we call declarative learning based programming.

• Lun-Wei Ku, Academia Sinica, Taiwan
• Cheng-Te Li, National Cheng Kung University, Taiwan
• Xin Huang, Hong Kong Baptist University, Hong Kong

SocialNLP is a new inter-disciplinary area of natural language processing (NLP) and social computing: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing. SocialNLP 2019 calls for research papers and data papers, and welcome your submissions.

• Ulrich Furbach, University of Koblenz
• Steffen Hölldobler, Technische Universität Dresden
• Marco Ragni, University of Freiburg
• Claudia Schon, University of Koblenz

Reasoning is a core ability in human cognition. Its power lies in the ability to theorize about the environment, to make implicit knowledge explicit, to generalize given knowledge and to gain new insights. This workshop brings together researchers who have an interest in the computational fundamentals of human reasoning. It's overall aim is to promote interdisciplinary research in the fields of artificial intelligence, automatic deduction, computational logics and the psychology of reasoning.

Coming soon

Strategic reasoning is one of the most active research area in multi-agent system domain. The literature in this field is extensive and provides a plethora of logics for modelling strategic ability. Theoretical results are now being used in many exciting domains, including software tools for information system security, robot teams with sophisticated adaptive strategies, and automatic players capable of beating expert human adversary, just to cite a few. All these examples share the challenge of developing novel theories and tools for agent-based reasoning that take into account the likely behaviour of adversaries. The SR international workshop aims to bring together researchers working on different aspects of strategic reasoning in computer science, both from a theoretical and a practical point of view.