计算机学院学术论坛报告
Academic Forum on Computer Science and Technology
特邀报告 第063期（总第179期）
主题报告：Inference Algebra and Machine Reasoning
报 告 人：Yingxu Wang [University of Calgary，Canada]
报告时间：9月 23日（周五）09: 30～11: 30
报告地点：上海大学延长校区行键楼734
邀 请 人：骆祥峰 副研究员
论坛主题：Inference as the basic mechanism of thought is one of the fundamental gifted abilities of human beings. Inference can be described as a cognitive process that creates rational causations between a pair of cause and effect based on empirical arguments, formal reasoning, and/or statistical regulations. Although there are various inference schemes and methods developed in a wide range of disciplines and applications, the framework of formal inferences can be described in five categories known as the relational, rule-based, logical, fuzzy logical, and causal inferences. With an extended expressive power, causal inferences are a set of advanced inference methodologies building upon other fundamental layers. The coherent framework of formal inferences reveals how human reasoning may be formalized and how machines may rigorously mimic the human inference mechanisms.
This talk presents a theory of formal inferences and a framework of causal inferences based on the denotational mathematical structure known as Inference Algebra (IA). The taxonomy and framework of formal causal inferences are explored in three categories: a) Logical inferences on Boolean and fuzzy causations; b) Analytic inferences on general functional, correlative, linear regressive, and nonlinear regressive causations; and c) Hybrid inferences on qualitative and quantitative causations. As that of Boolean algebra for explicit logical reasoning and fuzzy logic for approximate and uncertainty reasoning, IA is created as a denotational mathematical structure with a set of algebraic operators on a set of formal causations or logical, analytic, and hybrid inferences. In IA, the general forms of causations are rigorously modeled as the Boolean, fuzzy, functional, correlative, linear-regression, nonlinear-regression, qualitative, and quantitative causations. Eight algebraic inference operators (K) of IA are modeled for manipulating the formal causations. IA elicits and formalizes the common and empirical reasoning processes of humans in a rigorous form, which enable AI and computational intelligent systems to mimic and implement similar inference abilities of the brain by cognitive computing. A wide range of applications of IA are identified and demonstrated in cognitive informatics and computational intelligence towards novel theories and technologies for machine-enabled inferences and reasoning.
Biography：Yingxu Wang is professor of cognitive informatics and software engineering, President of International Institute of Cognitive Informatics and Cognitive Computing (IICICC), and Director of the Cognitive Informatics and Cognitive Computing Lab at the University of Calgary. He is a Fellow of WIF, a P.Eng of Canada, a Senior Member of IEEE and ACM, and a member of ISO/IEC JTC1 and the Canadian Advisory Committee (CAC) for ISO. He received a PhD in Software Engineering from the Nottingham Trent University, UK, and a BSc in Electrical Engineering from Shanghai Tiedao University. He has industrial experience since 1972 and has been a full professor since 1994. He was a visiting professor in the Computing Laboratory at Oxford University in 1995, Dept. of Computer Science at Stanford University in 2008, and the Berkeley Initiative in Soft Computing (BISC) Lab at University of California, Berkeley in 2008, respectively. He is the founder and steering committee chair of the annual IEEE International Conference on Cognitive Informatics (ICCI). He is founding Editor-in-Chief of International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), founding Editor-in- Chief of International Journal of Software Science and Computational Intelligence (IJSSCI), Associate Editor of IEEE Trans on System, Man, and Cybernetics (Part A), associate Editor-in-Chief of Journal of Advanced Mathematics and Applications, and Editor-in-Chief of CRC Book Series in Software Engineering. Prof. Wang is the initiator of a number of cutting-edge research fields or subject areas such as cognitive informatics, abstract intelligence, cognitive computing, cognitive computers, denotational mathematics (i.e., concept algebra, inference algebra, system algebra, real-time process algebra, granular algebra, and visual semantic algebra), software science (i.e., theoretical software engineering, unified mathematical models and laws of software, cognitive complexity of software, and automatic code generators), coordinative work organization theory, deductive semantics, the layered reference model of the brain (LRMB), the mathematical model of consciousness, the reference model of cognitive robots and autonomous agent systems, and built-in tests (BITs). He has published over 110 peer reviewed journal papers, 220+ peer reviewed full conference papers, and 16 books in cognitive informatics, software engineering, and computational intelligence. He is the recipient of dozens international awards on academic leadership, outstanding contributions, research achievement, best papers, and teaching in the last three decades.
，于工业技术研究院担任四年的研究人员，于1993年赴加拿大University of Calgary担任客座研究员，接着前往东华大学任教并为该校理工学院副院长，其间赴美国New Jersey Institute of Technology担任客座教授一年。
陈教授在研究、教学、以及公共服务方面曾获得许多奖项。迄今已发表百余篇国际期刊及研讨会论文、8本着作以及3项专利。陈教授参与一些国际专业及学术团体，先后主持20多个国际会议和研讨会。