Search Google Appliance


Courses One Page Flyer Spring 2008

SySc 513: Systems Approach

Provides a practitioner-oriented introduction to systems, including:

  • observer dependencies & context
  • meta-systems & subsystems
  • value systems and associated optimization/sub-optimization
  • life-cycle project management
  • inquiring systems
  • learning organizations
  • multiple perspectives.

Also explores qualitative aspects systems analysis tools such as graphs, structural modeling, and dynamic modeling.

For more information: Systems Approach Course Description

SySc 527/627: Discrete System Simulation

The mathematical basis for discrete system simulation (DSS) is probability theory and queuing theory. It is used extensively in the fields of operations research, civil engineering, industrial engineering, systems analysis, etc. Students learn how to use DSS to model systems of interest.

SySc 552/652: Game Theory

Game theory involves the study of cooperation and competition, without regard to the particular entities involved, and issues of rationality associated with such phenomena. The course presents the basic ideas of game theory, especially those concerning (a) 2-person zero-sum games, which the theory solves, and (b) 2- (or nequivalent-) person nonzero-sum games, which have no general solution and which often exhibit paradoxical features. Of particular substantive interest are dilemmas of collective action, which characterize many social, economic, and political problems. Of particular methodological interest are simulation techniques used to extend game-theory into domains where analytical results are impossible.

Also covered are (c) 2-person cooperative games (bargaining & arbitration), which have alternative plausible solutions; (d) coalition theory (nnon-equivalent-person games), in which analysis is complex and limited; and (e) social choice theory, which reveals the difficulties in integrating individual preferences into collective decisions. Emphasis in the course is on the findings of game theory, especially as they apply to the social sciences, rather than on the purely technical aspects of the theory.

SySc 553/653: Manufacturing System Simulation

The course focuses on using the ProModel discrete event simulation software to model manufacturing systems. Concepts include: a) overview of discrete system simulation and manufacturing simulation, b) data collection and prob. distributions, c) modeling material handling systems, d) job shop and production planning applications, and e) experimental design and output analysis. Relevant aspects of ProModel are also covered: locations, entities, processing logic, arrivals, path networks, resources, etc.

The course is designed to be of interest to students in Business, Engineering Management, Systems Science, Systems Engineering, and other programs; and to professionals in manufacturing, manufacturing engineering, and industrial engineering.

SySc 557/657 Artificial Life

"Artificial Life" (ALife) is a name given to theoretical, mathematical, and computationally "empirical" studies of phenomena commonly associated with "life," such as replication, metabolism, morphogenesis, learning, adaptation, and evolution. It focuses on the materiality-independent, i.e., abstract, bases of such phenomena. As such, it overlaps extensively with "theoretical biology" and, less extensively, with certain areas of physics and chemistry and the social sciences. It also raises important philosophical questions. It is part of a larger research program into "complex adaptive systems," one stream of contemporary systems theory.

In its intersection with computer science, ALife is the newest example of "the sciences of the artificial" (Herbert Simon). ALife is to life what AI is to intelligence. Christopher Langton writes that "Artificial Life ... complements the traditional biological sciences ... by attempting to synthesize life-like behaviors within computers and other artificial media." The purpose is twofold: to understand these phenomena better and to develop new computational technologies.

The course will sample the research literature in this field, and will be organized in a seminar format. Topics to be emphasized are: (1) discrete dynamics: cellular automata and random networks, (2) ecological & evolutionary dynamics, (3) genetic algorithm optimization and adaptation, (4) agent-based simulation. Other topics will include: artificial and real chemistry (metabolism, reproduction, & origin of life), "complex adaptive systems," autonomous agents, and philosophical issues.

SySc 576: AI: Neural Networks II

MW 4:00-5:50, Room TBA
George Lendaris, 725-4988 lendaris@sysc.pdx.edu

Focuses on applications. Topics in fuzzy set theory, control theory, and pattern recognition are studied and incorporated in considering neural networks. A Design project (using NN simulator) in selected application area is done by each student.

More Information: Neural Networks II, Spring 2008