Xixi Logic

Alexander Bochman

Holon Institute of Technology

Title: Assumption-Based Reasoning for the Next Generation

Abstract: We review the current state of the general theory of nonmonotonic reasoning and point out the key role of the assumption-based and causal reasoning for its future development.

Bio: Alexander Bochman is an associate professor at the Computer Science Department of Holon Institute of Technology (HIT), Israel. His main areas of interest are applications of logic in nonmonotonic and causal reasoning. He wrote three books on these subjects: A Logical Theory of Nonmonotonic Reasoning and Belief Change (Springer 2001), Explanatory Nonmonotonic Reasoning (World Scientific, 2005), and A Logical Theory of Causality (MIT Press, forthcoming in 2021).

Gabriella Pigozzi

Université Paris-Dauphine

Title: Spaces of argumentation and their interaction: some elements of thought inspired by the Covid-19 crisis in France

Abstract: During the Covid-19 pandemic, many public policy decisions had to be taken. These decisions were taking place in an unusual context and deciders needed to justify such decisions. As a consequence, we witnessed a very rapid construction and circulation of arguments in public spaces. One particularity in the Covid-19 debate is that most arguments were labelled as “scientific”. Yet, those arguments were debated in worlds different from science (such as journalists or network media) where the confrontation, the approval of arguments and the reasoning does not follow the same standards as in science.
To represent and analyze the consequences of this dynamic, we have recorded some arguments that circulated in the debate in France and can interestingly be tested against theories that have been developed in the literature on argumentation.

Bio: Gabriella Pigozzi is associate professor at LAMSADE (Université Paris-Dauphine). She works on formal approaches for collective decision-making, with contributions in the fields of belief revision, social choice theory, judgment aggregation, abstract argumentation and normative multi-agent systems.

Kun Kuang

Zhejiang University

Title: Causal Inference in Observational Studies

Abstract: Causal questions exist in many areas, such as health care, economics, political science, digital marketing, etc. Does a new medication lead to a better performance on a certain illness, compared with the old ones? Does a new marketing strategy improve the sales of a certain products? All these questions can be addressed by the causal inference technique. The gold standard approaches for causal inference are randomized experiments, for example, A/B testing. However, the fully randomized experiments are usually extremely expensive and sometimes even infeasible. Hence, it is highly demanding to develop automatic statistical approaches to infer causal effect in observational studies. In this talk, we show some new challenges of causal inference in the wild big data scenarios, including (1) high dimensional and noisy variables, (2) unknown model structure of interactions among variables, and (3) continuous/complex treatment variable. To address these challenges, we proposed Data-Driven Variable Decomposition (D2VD) algorithm, Decomposed Representation Counterfactual Regression (DeR-CFR) model, Differentiated Confounder Balancing (DCB) algorithm, and Generative Adversarial De-confounding (GAD) algorithm. We will show that our proposed algorithms can make a more precise and robust estimation of causal effect in observational studies, compared with the start-of-the-art methods.

Bio: Kun Kuang, Associate Professor in the College of Computer Science and Technology, Zhejiang University. He received his Ph.D. in the Department of Computer Science and Technology at Tsinghua University in 2019. He was a visiting scholar at Stanford University. His main research interests include causal inference and causally regularized machine learning. He has published over 30 papers in major international journals and conferences, including SIGKDD, ICML, ACM MM, AAAI, IJCAI, TKDE, TKDD, Engineering, and ICDM, etc.

Guilin Qi

Southeast University

Title: Reasoning in Knowledge Graphs

Abstract: Knowledge graph is important for realizing human intelligence as it provides structured knowledge and supports reasoning. It is based on logical languages, such as RDF(S) and OWL In this talk, I will firstly introduce the notion of Knowledge Graph and its logical foundation. I will then introduce our work on logical reasoning on knowledge graphs. Finally, I will introduce our work on knowledge representation learning in knowledge graphs.

Bio: Dr. Guilin Qi is a professor working at Southeast University in China. He is the head of the Knowledge Science and Engineering Lab and the director of institute of cognitive science at Southeast University. He is the executive Editor-in-Chief of Data Intelligence and an associate editor of Journal of Web Semantics. He received his PhD in Computer Science from Queen’s University of Belfast in 2006 and has worked in the Institute AIFB at University of Karlsruhe for three years. His research interests include knowledge representation and reasoning, knowledge graph, uncertainty reasoning, and semantic web. He has published over 150 papers in these areas, many of which published in proceedings of major conferences or journals. He has published a book on knowledge management for the semantic web in 2015. He has won the best-short paper runner-up award in CIKM 2017, and has a paper won the best-student paper award in ICTAI 2015. He is a workpackage leader of a EU FP7 Marie Curie IRSES project and a co-investigator of an ARC discovery project.

Marija Slavkovik

University of Bergen

Title: Reasoning about moral conflicts in AI

Abstract: AI systems of today operate in an open environment and should respect the norms and regulations in that environment. But whose rules? These systems can interact with multiple people and institutions which use all kinds of common sense reasoning rules to govern their behavior. These rules can be mutually conflicting and an AI system needs to be equip with a mechanism that resolves conflicts among rules. In this talk I discuss the problem of finding out what an AI system should do. We will go through past work and discuss the logical representation and reasoning challenges that are yet to be resolved.

Bio: Marija Slavkovik is an professor with the Faculty for Social Sciences of the University of Bergen. She is interested in problems of collective reasoning and decision making and machine ethics. She has held several seminars, tutorials and graduate courses on AI ethics (http://slavkovik.com/teaching.html). Marija is interested in the phenomenon of autonomous systems increasingly becoming moral arbitrators by virtue of the dissipation of the machine-society segregation. Automation, particularly of cognition, is not always possible without automating aspect of ethic reasoning or values. The problem then is whose moral values should have standing, what moral opinions and values should be elicited, how should that be done and what is the right way to aggregate these “measurements”?