Session:

Multi-Agent Systems
Tuesday, March 02, 2004, 17.15 – 17.35

Session Chair:

Jorge Marx-Gomez

Paper Title:

Obsage - An Agent-based Model of Observational Learning

Author(s):

K. Jones, Rochester Institute of Technology, U.S.A.
Dr. M.Van Wie, Rochester Institute of Technology, U.S.A.

Abstract:

In many social systems, individuals are able to learn new behaviors by observing others. This paper describes experiments in augmenting reinforcement learners in an artificial society with the ability to observe each others' actions and rewards; the initial hypothesis was that observant agents would have an advantage over their non-observant counterparts. Experimentation shows that observation helps most when experienced teachers are available, and is most effective early in an agent's life-cycle.