Arthur Farley

Arthur Farley profile picture
  • Title: Professor Emeritus
  • Phone: 541-346-1387
  • Office: 258 Deschutes Hall
  • Research Areas: Communication Networks, Artificial Intelligence
  • Website: Website

Education

  • BS, 1968, Rensselaer Polytechnic Institute
  • PhD, 1974, Carnegie-Mellon

Biography

Art Farley received a B.S. Degree from Rennselaer Polytechnic Institute in 1968 and graduated from Carnegie Mellon University with a Ph.D. in Computer Science in 1974. Since then he has been a faculty member in the Department of Computer and Information Science at the University of Oregon. His research interests have been in artificial intelligence and graph theoretical models of communication networks. Realted to artificial intelligence, he has conducted research on problem solving and planning, qualitative reasoning, models of argumentation, and artificial evolution. In networks, his research has considered issues of efficient network design for both message dissemination and robustness of network connection as well as basic questions in graph theory.

Research

Dr. Arthur M. Farley is currently conducting research in two areas:
  • Artificial Evolution, and
  • Algorithmic Graph Theory.
Artificial evolution studies evolution in an abstract, simplified setting, illuminating many of the complexities of environments, individuals, and genetic representations in the hope of uncovering the basic impacts of various processes on the dynamics of evolution. As a field of study, artificial evolution sits between the study of biological evolution and applied evolutionary computation. The study of biological evolution must attempt to model a wide range of natural factors including the complexity of DNA and its chemical reaction with enzymes producing specialized proteins all the way to interactions between individuals and environments. We are considering questions of developmental learning, dominance, and structured populations.
 
Algorithmic graph theory considers extremal problems on graphs, determining optimal graph structures for given tasks or optimal algorithms on given graph structures. We are considering questions of graphs where distances reflect importance of vertices and which are resilient to certain forms of attack. Substructures that allow efficient group communication are also of interest.