Workshop 1: Principle and practice of data and Knowledge Acquisition Workshop (PKAW 2022)

Introduction: Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW2022 will continue the above focus and welcome the contributions on the multi-disciplinary approach of human and big data-driven knowledge acquisition, as well as AI techniques and applications. AI is changing the way in which organizations innovate and communicate their processes, products, and services. Also, in our daily life, AI-embedded devices such as smart speakers are about to become widely used, which extends the possibility of acquiring knowledge from users’ behavior observed through the interaction between those devices and their users. Knowledge acquisition and learning from big data are becoming more challenging than ever. Various knowledge can be acquired not only from human experts but also from heterogeneous data. Multidisciplinary research, including knowledge engineering, artificial intelligence and machine learning, human-computer interaction, etc., is required to meet the challenge. We invite authors to submit papers on all aspects of these areas. Furthermore, not only in the engineering field but also in the social science field (e.g., economics, social networks, and sociology), recent progress in knowledge acquisition and data engineering techniques is realizing interesting applications. We also invite submissions that present applications tested and deployed in real-life settings and lessons learned during this process.

Organizers: Qing Liu, Wenli Yang, and Shiqing Wu

Website: https://pkawwebsite.github.io/2022/

Workshop 2: Decoding Models of Human Emotion Using Brain Signals

Introduction: Affective intelligence is becoming a growing important component in the development of artificial intelligence, which plays a critical role in the automatic and interactive processes such as human-computer interaction and human-robot interaction. The need for technologies that are capable of automatically, dynamically, and reliably decoding human emotions is increasing dramatically. However, due to the complexity and diversity of human emotions, it is still difficult to accurately estimate emotion. From a neurophysiological view, brain signals provide a more natural, direct, and objective approach to decode the real human emotions of individuals. In recent years, emotion decoding from brain signals has become an attractive and purposeful research topic. Various brain recording techniques, such as electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), functional Near-Infrared Spectroscopy (fNIRS), and magnetoencephalography (MEG), have been introduced to differentiate the underlying emotions from the recorded ongoing brain signals, where the tremendous potentials in emotion decoding have been widely evidenced. Considering the contaminated noises in the brain signals and the existence of individual differences, there still needs great effort to propose more valid, reliable, and practical emotion decoding models, and more comprehensive and dedicated model explorations and comparisons should be conducted. This workshop seeks various new feature extraction and modeling methods that could help to improve the performance of emotion decoding from brain signals.

Organizers: Zhen Liang and Zhiguo Zhang

Website: https://sites.google.com/view/pricai2022-emotion-brain

Workshop 3: The 1st International Workshop on Democracy and AI (DemocrAI2022)

Introduction: Many tools and methodologies are being developed for online-based large-scale crowd collaboration and decision-making to realize new type of democracy. Compared to pre-Internet-era methodologies, they excel at overcoming limitations as may arise from geographical, cultural, religious, and ethnic issues. AI-assisted democracy is particularly promising. It pledges to take full advantage of AI's recourse to massive knowledge bases and 24/7 availability in supporting humans' collaborative activities, helping them to: gather together; share experiences; and come out with new knowledge and skills. Moreover, it is expected that AI technologies will detect and curb aggressive behaviors such as flaming and promulgation of fake news to realize trustful democracy.

Success in AI-based democracy platforms depends on many desiderata including: availability of platforms for democratic decision-making; formal theories of democratic collaboration for attaining smart agents; methodologies to evaluate democratic discussion and decision making; socio-psychological understanding of democracy; identification and resolution of ethical/legal issues around it. Indeed, democracy is a complex process that also involves many spatio-temporal constraints and concurrent activities by a large number of participants. AIs should be smart enough to automatically resolve these issues. To this end, different technologies are being investigated: multi-agent systems, reinforcement learning, deep-learning, game theory and mechanism design, argumentation theories, social choice theory, graphical utility models, case-based reasoning, optimization, and predicting and learning methods. A number of applications of AI-based collaboration are foreseeable, including but not limited to: hyper-democracy, smart societies, automated negotiations, decision-making support tools, negotiation support tools, consensus support tools, collaboration tools as well as knowledge discovery and educational learning tools. The aim of this workshop, hence, is to discuss agent-based methodologies, tools, relevant formal theories, and ethical/legal/socio-psychological issues that push forward the state-of-the-art of crowd collaboration and decision-making.

Organizers: Takayuki Ito, Ryuta Arisaka, Rafik Hadfi, Takahiro Uchiya, Tokuro Matsuo, Susumu Ohnuma, and Shun Shiramatsu

Website: https://sites.google.com/view/democrai2022