CS 329 Intelligent Simulation and Its Applications Stanford University Autumn 1998

Contents:

Time: T/Th 1:15-2:30
Location: Gates 100
Units: 3
Class Web Page: http://www.ksl.stanford.edu/projects/classes/cs329

Instructor:

Feng Zhao, PhD
Member of Research Staff, Xerox Palo Alto Research Center
Associate Professor of Computer and Information Science, Ohio State University
Xerox Palo Alto Research Center
3333 Coyote Hill Road
Palo Alto, CA 94304
Tel. 650-812-5078
Email: zhao@parc.com
URL: http://www.parc.com/zhao
Office Hours: Thursday 2:30-3:30

TA:

Lise Getoor
Gates 126A
Email: getoor@robotics.stanford.edu
Office Hours: Thursday 10-12

Course Description

Introduction to AI representations, algorithms, and programming methodologies for preparing and interpreting computational experiments in science and engineering. Focus on mixed model-based and data-driven techniques and case studies. Topics: numeric fields, imagistic reasoning, spatial aggregation algorithms and language, cognitive foundation and relation to visual and spatial reasoning, scientific data mining, qualitative physics ontologies, mixed numeric, geometric, and symbolic computation, application studies of KAM (nonlinear dynamics), MAPS (control synthesis), HIPAIR (mechanical mechanism design), Fluid Dynamicist Workbench, and weather data analysis, and other research frontiers. Hands on experience with SAL/C++ programming environment.

Course Objectives

Learn basic qualitative/quantitative techniques for representing and reasoning about physical systems.
Know how to combine AI methods with symbolic and numeric techniques to prepare and interpret computational experiments.
Examine several interesting, non-trivial domains in science and engineering and gain experience in computational implementations.

Prerequisites

Familiarity with basic AI concepts, algorithms, data structures, and programming (such as C/C++, scheme, or LISP).
Prospective students are encouraged to seek permission from the instructor.

Course Notes and Handouts

Course Notes and supplementary readings will be available from the Stanford Bookstore Course Materials section.

Who should take the class

The course can be used to meet the AI concentration requirement for MS in Computer Science.

It will be suitable for graduate or advanced undergraduate students who have had a basic exposure to computational intelligence and are interested in learning more about advanced applications of AI and scientific computation. It is expected to attract students from areas such as qualitative reasoning, data mining, model-based reasoning and diagnosis, spatial and temporal reasoning, diagrammatic reasoning, visual problem solving, as well as engineering disciplines such as control, diagnostics, manufacturing, engineering design and simulation.

Prof. Zhao taught the course at the Computer and Information Science Department of the Ohio State University. A subset of the course materials was also taught at the 1998 National Conference of American Association for Artificial Intelligence (AAAI-98) Tutorial Forum.