CH751 퍼지시스템 특강 Uncertainties in Intelligent Systems

1. CH751 퍼지시스템 특강Uncertainties in Intelligent Systems 2004년도 제 1학기

2. 강의진 소개 • 담당 교수 • 조성배(공대 C515;  2123-2720; sbcho@cs.yonsei.ac.kr) • 웹 페이지 : http://sclab.yonsei.ac.kr/Courses/04FuSys • 강의 시간 • 화 6, 7, 목 7 (C520) • 면담 시간 • 월 8, 9, 수 9 • 담당 조교 • 황금성

3. Uncertainties in Intelligent Systems • Dealing with uncertain and imprecise information has been one of the major issues in almost all intelligent system • Decision making systems, diagnostic systems, intelligent agent systems, planning systems, data mining, etc • Various approaches to cope with uncertain, imprecise, vague, and even inconsistent information • Bayesian and probabilistic methods, belief networks, softcomputing, etc • Softcomputing • Neural networks, fuzzy theory, approximate reasoning, derivative-free optimization methods (GA), etc • Synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance  intelligent systems to mimic human intelligence in thinking, learning, reasoning, etc

4. Course Schedule (1) • 3/2, 3/4 : 과목소개 및 SC/AI/KE 개요 • 3/9, 3/11 : Rule-based systems, expert systems, fuzzy systems • 3/16, 3/18 : Knowledge representation • 3/23, 3/25 : Uncertainties in knowledge-based systems • 3/30, 4/1 : Bayesian Network 특강 • 4/6, 4/8 : 1차 프로그래밍 과제 • 4/13, 4/15 : Machine learning methods for knowledge engineering • 4/20 : 중간시험 • 4/29 : 프로젝트 제안서 발표 • 5/6 : Fuzzy sets and fuzzy logic • 5/13 : Fuzzy systems • 5/20 : Fuzzy system applications • 5/27 : Introduction to neural networks • 6/3 : Hybrid systems • 6/10 : 프로젝트 결과 발표 • 6/15 : 기말시험

5. Course Schedule (2) http://www.csse.monash.edu.au/~annn/443/443.html • 3/25 : Introduction to Bayesian AI • 3/30, 4/1 : Introduction to Bayesian Networks • 4/6, 4/8 : Inference in Bayesian Networks • 4/13 : Decision Networks • 4/20 : 중간시험 • 4/27 : BN Knowledge Engineering • 5/4 : BN Case Studies • 5/11 : Intro to Machine Learning and Bayesian Confirmation Theory • 5/18 : Linear Causal Models, Conditional Independence Learning • 5/25 : Parameter learning, Metric Learning: Bayesian, MDL, MML • 6/1 : Search and the Evaluation of BN Learners • 6/8, 6/10 : 프로젝트 결과 발표 • 6/15 : 기말시험

6. Reasoning Under Uncertainty Part I : This part of the course will focus on two representations for modelling and reasoning under uncertainty: Bayesian (or Belief) networks and Markov Decision Processes. Bayesian networks have rapidly become one of the leading technologies for applying AI to real world problems. This follows the work of Pearl, Lauritzen, and others in the late 1980s showing that Bayesian reasoning in practice could be tractable (although in principle it is NP-hard). We begin with a brief examination of the philosophy of Bayesianism, motivating the use of probabilities in decision making, agent modeling and data analysis, and contrasting Bayesian methods with certainty factors, fuzzy logic and the Dempster-Shafer calculus. We introduce Bayesian networks, their inference techniques and approximation methods. We look at an extension to Bayesian networks, called decision networks, which support decision making. Several BN software packages will be introduced and used throughout the course. We will look at the general problem of "knowledge engineering" of Bayesian networks, and consider practical issues of eliciting domain knowledge from experts. These issues will be illustrated with through the use of several real-world case studies, including Bayesian poker, seabreeze prediction and an intelligent tutoring system for decimal misconceptions. This part of the course will conclude with a brief look at another representation of uncertainty, Markov Decision Processes, together with basic dynamic programming solution methods

7. Reasoning Under Uncertainty Part II : There are many difficulties with constructing AI models (such as BNs or MDPs) using human domain knowledge, including lack of human domain expertise, difficulties in elicting causal structure and inconsistent probabilities. This has led to a strong interest in automating the learning of such models from statistical data, which is the focus of the second part of the course. We will start with an introduction to machine learning concepts, including Bayesian confirmation theory, and their application to classifier systems and MDPs with reinforcement learning. We with then examine paremeter learning in the context of Bayesian net parameterization. These techniques allow much of the difficult part of knowledge engineering with Bayesian nets to be automated, but leaves the problem of sorting out Bayesian net structure untouched, so we will continue with Bayesian net structure learning. Some of the techniques have been around for a century; we will look briefly at the tradition of structural equation modeling and causal modeling in the social sciences. Then we examine very recently developed Bayesian, MDL and MML methods for learning causal structure