"Multi-agent systems as stochastic systems: a mathematical approach"

4/13/2001: [time not recorded]
[location not recorded]

Abstract: Complex collective behavior can often arise out of local interactions between many simple entities, as has been observed in many natural systems. In recent years multi-agent and the robotics communities have begun to explore such biologically inspired systems. These systems offer many advantages over traditional multi-agent systems that are based on deliberative agents and centralized control -- they are efficient, robust, and scalable. Another advantage is that the collective behavior in these systems is often amenable to quantitative mathematical analysis, which can be used to predict behavior of even very large systems and to optimize system performance. While the behavior of an individual agent can be very complex and subject to many unpredictable influences, the behavior of the multi-agent system can often be captured by a simple probabilistic model. We explore a family of such mathematical models that describe the macroscopic or collective dynamics of a multi-agent system. We illustrate our approach by applying it to analyze several agent-based systems. Our examples include coalition formation in an electronic marketplace, and foraging and cooperative task completion in a group of robots.

About Kristina Lerman: Kristina Lerman received her Ph.D. in physics in 1995 from University of California at Santa Barbara, where her research focused on the experimental study of complex dynamical systems. She joined the USC Information Sciences Institute in 1998 as a research scientist. Her current research focuses on two main topics: mathematical modeling of multi-agent systems and applications of machine learning to information extraction and text analysis.

Last updated: Mon Jun 19 17:44:06 2006