Workshops
Workshop ＃1 : 2024 Principle and practice of data and Knowledge Acquisition Workshop (PKAW 2024)
Introduction :
PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-art in the areas of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW 2024 will continue the above focus and welcome the contributions to the multi-disciplinary approach of human and big data-driven knowledge acquisition and 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.
Organizers :
Shiqing Wu, University of Technology Sydney, Australia (
Xing Su, Beijing University of Technology, China (
Website :
https://pkawwebsite.github.io/2024/
Workshop #2 : The 9th Linguistic and Cognitive Approaches to Dialogue Agents (LaCATODA2024)
Introduction :
The more human-like machine intelligence engineers develop, the more important is for them to be familiar with advances in fields traditionally focusing on humans — ethics, psychology, linguistics, or cognitive science. In the age of data explosion, advancing hardware and more powerful learning algorithms, it has been becoming obvious that we need to study mechanisms underlying what we call a natural dialog, how we track a conversation or what we remember. It is not enough to pay attention what information is conveyed but also how it is conveyed. For this reason we extend topics to knowledge-related topics to seek answers to questions like how an utterance can become harmful, amusing, beautiful or interesting. We aim to gather AI researchers who realize that in spite of current popularity of GenAI "chatbots", they are not really dialog systems and it is necessary to extend existing and propose new algorithms to perform natural conversation. We will call for papers regarding research not only on the latest trends but also on revisiting classic studies related to dialog and understanding, as the AI developments allow to utilize theories that had focused on human interaction and understanding in the past. The workshop intends to spark an interdisciplinary discussion on affect in dialog understanding and generation tasks.
Organizers :
Rafal Rzepka, Hokkaido University, Japan (
Michal Ptaszynski, Kitami institute of Technology, Japan (
Pawel Dybala, Jagiellonian University, Poland (
Siaw-Fong Chung, National Chengchi University, Taiwan (
Jordi Vallverdú, Autonomous University of Barcelona, Spain (
Website :
https://sites.google.com/view/lacatoda2024
Workshop #3 : The 1st International Workshop on Educational Artificial Intelligence (IWEAI 2024)
Introduction :
The 1st International Workshop on Educational Artificial Intelligence (IWEAI 2024) is dedicated to exploring the transformative impact of AI in education. We aim to bring together leading researchers, educators, and technologists to discuss the latest advancements, ethical considerations, and practical applications of AI in educational settings. The IWEAI 2024 workshop strongly aligns with the overarching theme of advancing AI technology and its practical applications, as emphasized by PRICAI 2024. Within the broader field of AI, educational AI plays a significant role, and this workshop will make a meaningful contribution to the overarching discourse of PRICAI 2024 by achieving the following: Showcasing Educational AI Applications: IWEAI 2024 will spotlight how AI technologies can be tailored and effectively implemented in educational contexts, demonstrating their real-world applications. Promoting Ethical AI Practices: In line with PRICAI 2024's dedication to ethical AI, our workshop will delve into the ethical considerations surrounding AI in education, ensuring responsible and ethical use of AI. Encouraging Multidisciplinary Insights: By bridging the gap between AI technological advancements and practical educational needs, the workshop aims to foster a comprehensive understanding of AI's societal potential, embracing a multidisciplinary perspective. In summary, IWEAI 2024 will serve as a valuable complement to PRICAI 2024, offering unique insights into the pivotal role of AI in transforming education while aligning with PRICAI's broader mission of advancing AI technology and its applications.
Organizers :
Yuncheng Jiang, South China Normal University, China (
Gang Li, Deakin University, Australia (
Website :
https://iweai.github.io/2024/home
Workshop #4 : Representation Learning and Clustering (RLC’24)
Introduction :
Data clustering and representation learning are essential in data science, aiding in the exploration of massive datasets across fields like information retrieval, natural language processing, bioinformatics, recommender systems, and computer vision. However, traditional clustering methods struggle with the high dimensionality, noise, heterogeneity, and sparsity of data from modern applications, especially when data is collected from multiple sources or represented by multiple views. This has led researchers to develop new deep clustering models, often relying on representation learning. These models embed original data into a low-dimensional latent space for clustering, either sequentially or jointly, to handle complex data more effectively. The primary goal of this workshop is to unite leading researchers and practitioners working on advanced deep unsupervised feature extraction and clustering models. The workshop aims to: 1. Present recent advances in representation learning and clustering methods, including multi-view clustering and semi-supervised learning. 2. Highlight potential applications that could inspire new deep approaches. 3. Explore benchmark data to better evaluate and study deep clustering models. 4. Compare the effectiveness of deep clustering models with classical approaches in terms of interpretability of clusters and scalability. The RLC workshop seeks to advance research at the intersection of representation learning and clustering, focusing on real-world data science challenges. It welcomes high-quality academic and practical papers on unsupervised graph representation learning for clustering and related work.
Organizers :
Lazhar Labiod, Université Paris Cité, France (
Mohamed Nadif, Université Paris Cité, France (
Website :
https://sites.google.com/view/rlc24-pricai-workshop/home
Workshop #5 : The 5th International Workshop on Democracy and AI (DemocrAI24-Winter)
Introduction :
AI-empowered systems designed to support digital participation in decision-making are rapidly becoming more intelligent in the functionality they offer to users, ultimately benefiting humanity. For example, AI-empowered online forums have revolutionized how we engage in processes, disseminate and generate information, and make decisions. What sets modern AI apart is its unparalleled ability to manage vast quantities of information in various formats, collaboratively (through Human-AI teaming) and independently. However, like all technologies, AI also presents a dual nature, offering opportunities and challenges. This duality necessitates careful management to maximize benefits while mitigating risks. To address these complexities, cross-disciplinary research is crucial. It can highlight the advantages and tackle the pressing challenges associated with AI tools. This workshop aims to enable interdisciplinary research by inviting researchers from diverse backgrounds spanning social science and AI research domains. Our goal is to facilitate the exchange of insights and experiences, identify emerging trends, and foster collaboration across various strands of AI and social research.
Organizers :
Takayuki Ito, Kyoto University, Japan (
Jawad Haqbeen, Kyoto University, Japan (
Rafik Hadfi, Kyoto University, Japan (
Sofia Sahab, Kyoto University, Japan (
Susumu Ohnuma, Hokkaido University, Japan (
Tokuro Matsuo, Advanced Institute of Industrial Technology, Japan (
Website :
https://sites.google.com/view/pricai-democrai-24
Workshop #6 : Pacific-Rim International Workshop on Applied Knowledge Graphs (PRIWAKG-2024)
Introduction :
The goal of this workshop is to facilitate practitioners in sharing their experiences, best practices, challenges, opportunities and lessons learned. Focus is on “Applied” Knowledge Graphs (KGs). This means all the presentation/demo will be on applying Knowledge Graphs work to use cases from Government, Industry, Non-Profit-Organisation, etc.
Organizers :
Dickson Lukose (
Prasad Yalamanchi (
Website :
Workshop #7 : Artificial Intelligence for Sustainability (AI4S)
Introduction :
Artificial Intelligence (AI) and especially generative AI has a potential to help us in many ways, including addressing the 17 UN sustainability goals, producing smarter and greener hardware, software and applications. However, it produces also various kinds of waste and impacts to manage. Sustainability requires solving complex problems, often with hybrid AI architecture. In numerous cases knowledge is vital for successful innovation, facing environmental challenges, in smart cities, education and resource management, among others. Energy life cycle, optimization, and combination of renewable energies for sustainable powering and heating can be improved applying also Knowledge Management principles. Raising prices of natural gas drives growth in renewable energies. Industries like steel, glass, chemical manufacturing sites are they are looking for AI based control systems for the Grid connected micro-grids. Circular energy explores the heat generated by data centers and the waste steam for a sustainable environment as like creating fish farms or agricultural sites, or recreating cooling water, which are additional processes that demand AI-based scheduling using meteorological data. Clean energy solutions require evolution of manufacturing processes and holistic approach supported by rigorous analytics, high quality data and the recognition of infrastructure dependencies. In order to increase sustainability in agriculture, it is vital to incorporate green technologies into farming, e.g., renewable generation. However, there are significant challenges associated with enhancing the sustainability of agriculture.
Organizers :
Eunika Mercier-Laurent (
Gülgün Kayakutlu
Mieczyslaw Lech Owoc
Abdul Wahid
Website :
https://sites.google.com/view/ai4s-pricai24/
Workshop #8 : 2024 Workshop on AI-empowered Systems Simulation (WASS 2024)
Introduction :
System simulations have become increasingly complex in recent years, aiming to replicate real-world scenarios with greater fidelity. However, traditional simulation methods often encounter limitations in accuracy, efficiency, and adaptability. These challenges are compounded by the growing demand for simulations that can keep pace with the rapidly evolving technological and industrial landscapes. Integrating artificial intelligence (AI) into system simulations promises to overcome these challenges. AI algorithms can enhance simulations' realism and predictive power by incorporating dynamic, data-driven models that learn and adapt over time. AI-empowered simulations have the potential to revolutionize various industries, from engineering and healthcare to economics and social science, by enabling more precise predictions, optimizations, and decision-making processes. Therefore, the Workshop of AI-empowered Systems Simulation (WASS 2024) aims to bring together researchers, practitioners, and industry experts to explore the convergence of AI and system simulations. We invite submissions that address theoretical frameworks, methodologies, and practical applications related to integrating AI in simulations.
Organizers :
Hang Xiong, Huazhong Agricultural University, China (
Peng Lv, Central South University, China (
Zhou He, University of Chinese Academy of Sciences, China (
Yuxuan Hu, University of Tasmania, Australia (
Website :
https://wasswebsite.github.io/index.html