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Artificial Intelligence Association of Thailand (AIAT)
Sirindhorn International Institute of Technology, Thammasat University (SIIT, TU)
Thammasat University (TU)
Prince of Songkla University
Thailand National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA)


The Pacific Rim International Conferences on Artificial Intelligence (PRICAI)
Artificial Intelligence Journal
Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR)
Thammasat University
Thailand Convention and Exhibition Bureau (TCEB) OR
Thailand Convention and Exhibition Bureau (TCEB)
Provincial Electricity Authority of Thailand (PEA)
Sertis Co., Ltd.
Defence Technology Institute (DTI) (Thailand Public Organization)
Franz Inc.
The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)


Lecture Notes in Computer Science (LNCS)
Artificial Intelligence Association of Thailand (AIAT)

Keynote Talks

Two PRICAI Keynote/Invited Speakers

Sheng-Chuan Wu

Franz Inc., Silicon Valley, USA.


Global Data Warming for AI Spring

The explosion of data may have made AI trendy again. Google has just made their AI chief the head of Google Search, which holds the world's biggest repository of data. One of the key AI endeavors is knowledge acquisition and discovery. Typically, we turn the data we collect into information by applying its context. We then interpret the information to derive knowledge from it. Knowledge is what provides value to our endeavors, as we believe. Is this paradigm still true with the explosive growth in Big Data? One of the most consequential examples is medical science. Since the sequencing of the human genome in 2003, we have dreamed about treating patients more effectively based on their genomic profiles. Such a dream remains elusive. The fundamental difficulty lies in the complexity of biological systems that have evolved through billions of years. On the other hand, major progress can be and has been made in "personalized medicine" by applying classic AI machine learning on the massive patient medical data accumulated. In essence, we can uncover new knowledge from the data to help patients without knowing the why a priori. Lack of direct value brought forth the last AI winter. Perhaps Big Data will foreshadow the coming spring of AI. Exploiting Big Data brings another set of management problems, namely the heterogeneous nature of data sources and taxonomies, the enormous size of data volume, and huge data analytic processing requirements. We will discuss all these issues and show some examples in healthcare at this talk.


Dr. Sheng-Chuan Wu received his Ph.D. in Scientific Computing and Computer Graphics from Cornell University in the US. He has, since graduation, involved in several software companies, including the founding of the first integrated CAD/CAM/CAE company. In the last 20 years, he worked as a senior corporate executive at the leading Artificial Intelligence and Semantic Technology company, Franz Inc in Silicon Valley, with responsibility in application development, marketing, consulting and new business development. Dr. Wu has also in many occasions collaborated with Bioinformatics experts from Harvard Medical School, Stanford University and Astra Zeneca, working with massive biological data. Dr. Wu has been focusing on Semantic Technology over the last 8 years. He routinely lectured on AI and Semantic Technology at conferences. He was a keynote speaker at PRICAI 2004 in Auckland NZ. Most recently, he gave a keynote at KSEM 2015 in China and will deliver another keynote at KMO 2016 in Germany. He has, since 2007, conducted more than 20 week-long workshops on Semantic Technology and Artificial Intelligence in Malaysia, China, Singapore, India and other Asian countries. Dr. Wu has also consulted on several Big Data and Semantic Technology projects in the US and Asia. Some of the projects include: Biodiversity Repository, Precision Agriculture for Citrus Plantation, Telecom Customer Relation Management, Malaysia R&D Knowledgebase, Intelligence analytics, Meta Data Management and E-Learning System.

Zhi-Hua Zhou

Nanjing University, China


From AdaBoost to Optimal Margin Distribution Machines

AdaBoost is a famous mainstream ensemble learning approach that has greatly influenced machine learning and related areas. A well-known mystery of Adaboost lies in the phenomenon that it seems resistant to overfitting, which has inspired a lot of theoretical investigations. In this talk, we will briefly introduce the margin theory that has a long history of debating but recently defensed. We will show how the theoretical findings provide inspiration for Optimal margin Distribution Machines (ODM), a promising direction of designing powerful learning algorithms


Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University. He authored the book "Ensemble Methods: Foundations and Algorithms", and published more than 100 papers in top-tier journals and conference proceedings. His work have received more than 20,000 citations, with a h-index of 71. He also holds 14 patents and has good experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, the IEEE CIS Outstanding Early Career Award, the Microsoft Professorship Award, 12 international journal/conference paper/presentation/competition awards, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China, and Associate Editor of ACM TIST, IEEE TNNLS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General Chair and Program Chairs for various conferences including PAKDD'07, PRICAI'08, SDM'13, ICDM'15, IJCAI'15 Machine Learning track, etc. He also serves as Advisory Committee member for IJCAI 2015-2016, and Steering Committee Member of PAKDD and PRICAI. He is a Fellow of the AAAI, IEEE, IAPR, IET/IEE, CCF, and an ACM Distinguished Scientist.

ONE Co-PRICAI/PRIMA Invited Speaker

Phan Minh Dung

Asian Institute of Technology, Thailand


Argumentation for Practical Reasoning

Argumentation is a key component in human intellectual activities. We engage in argumentation in almost all of our daily conversations. Much of our knowledge and intellectual skills is learned through argumentation. Can we build humanoid robots that could argue with us and help us improving our own arguing skills? Argumentation is an active field in AI and knowledge representation. A well-known model of an argumentation systems (also known as argumentation frameworks) is a pair (AR,attack) where AR is a set of arguments and att is a binary relation over AR representing an attack relation between arguments. Though very simple, it turns out that this model is general enough to represent many forms of practical reasoning like legal reasoning, common-sense or default reasoning, experimental reasoning (like in medicine). In this talk we will present an axiomatic approach to the theory of argumentation. We begin with a rather light-hearted illustration of the role of argumentation in our daily lives and proceed to present an overview of the principles and properties underlining the theory of argumentation.


Currently I am a professor in computer science at AIT. Before joining AIT, I have been working at the National Institute for Informatics in Hanoi Vietnam. I got my Master and PhD degrees at the Dresden University in Germany. The title of my PhD thesis is: Structure and Axioms of Nondeterministic Computation. I am an associate editor of the Journal of Artificial Intelligence and an area editor of the Journal of Logic and Computation. I am a member of the editorial board of the journals of Theory and Practice of Logic Programming and Journal of Argument and Computation.

Two PRIMA Keynote/Invited Speakers

Joerg P. Mueller

Department of Informatics, Clausthal University of Technology

Email: joerg.mueller at

Agent-based modelling and simulation for co-operative traffic and transport

Recent developments in Car-to-X communication networks, advanced assistance functions, and autonous vehicles create new prospects for Intelligent Traffic and Transport Systems (ITS): Traffic Information Systems may benefit from real-time information provided via cooperative sensing; autonomous vehicles can interact and perform cooperative driving maneuvers, aiming at improving traffic safety, traffic efficiency, and user comfort; traffic management may provide individual route guidance and electronic road pricing mechanisms to influence the behaviour of (automated and human) traffic participants towards societal goals. However, the autonomy of traffic participants makes analysing, predicting, and managing future ITS a very difficult problem, creating challenges for next-generation traffic management systems, requiring new cooperative approaches, enabling us to reconcile the concepts of user optimum and system optimum, respectively. Starting from the notion of co-operative, (de-)centralized traffic management, I advocate the multi-agent paradigm for modeling and simulation of future traffic systems. I report on experiences with a multi-agent based approach for cooperative traffic management and autonomous vehicle scenarios. I sketch the underlying conceptual paradigm and architecture, exemplify the use of game-theoretic and computational social choice methods in the traffic application domain, and report on ongoing work geared towards platforms supporting scalable multi-agent based simulations.


Prof. Dr. Joerg P. Mueller is a Full Professor of Computer Science at Clausthal University of Technology; since 2008, he has been Head of the Department of Informatics at TU Clausthal. Previously, Joerg was a senior researcher at Siemens AG Corporate Technology, where he led the agents and peer-to-peer computing research group; earlier employments include John Wiley & Sons, Zuno Ltd., Mitsubishi Electric, and the German Artificial Intelligence Research Center (DFKI). Joerg holds a Ph.D. from Saarbrucken University in 1996 and an M.Sc. in Computer Science from Kaiserslautern University in 1991. Within the last 25 years, he has published over 200 papers on intelligent agents and multi-agent systems, business information systems and distributed computing. His current research interests include agent-based models, methods, technologies and applications for decentralized sociotechnical systems, which are developed and validated in traffic/transport, automation, and product engineering applications.

Toru Ishida

Department of Social Informatics, Kyoto University

Email: ishida at

Intercultural Collaboration: Human-Aware Research on Multiagent Systems

In the beginning of the new millennium, we proposed the concept of intercultural collaboration where participants with different cultures and languages work together towards shared goals. Because intercultural collaboration is a new area with scarce data, it was necessary to execute parallel experiments in both in real fields as well as in research laboratories. In 2002, we conducted a one-year experiment with Japanese, Chinese, Korean and Malaysian colleagues and students to develop open-source software using machine translation. From this experiment, we understood the necessity of language infrastructure on the Internet to create customized multilingual environments for various situations. In 2006, we launched the Language Grid project to realize a federated operation of servers for language services. So far, four servers have been set up in Asia, and more than 200 language services have been registered from 22 countries. Using the Language Grid, we have been working with an international NGO for four years to support communications between rice harvesting experts in Japan and farmers and their children in Vietnam. During these experiences, we gradually understood the nature of intercultural collaboration and we faced different types of difficulties. Problems are "wicked" and not easily defined because of their nested and open networked structure. For example, technologies supporting collaboration are often successful when we weaken cultural differences; cognitions of cultural differences are different in different cultures, etc. In this talk, we view intercultural collaboration as a research in human-aware multiagent systems with the goal to encourage researchers in multiagent systems to stand by intercultural collaboration. We believe a human-aware multiagent research can contribute solutions to real-world complex problems.


Toru Ishida has been a professor of Kyoto University since 1993. He has been a fellow of IEEE, a vice president of IEICE, and a member of the Science Council of Japan. He is a co-founder of the Department of Social Informatics, Kyoto University and the Kyoto University Design School. His research interest lies with Autonomous Agents and Multi-Agent Systems and modeling collaboration within human societies. He contributed to create PRIMA/ICMAS/AAMAS conferences: he was a chair of the first PRIMA, a program co-chair of the second ICMAS, and the general co-chair of the first AAMAS. His projects include Community Computing, Digital City Kyoto, Intercultural Collaboration Experiments, and the Language Grid.