Evolving Behaviors for Cooperating Agents

A good deal of progress has been made in the past few years in the design and implementation of control programs for autonomous agents. A natural extension of this work is to consider solving difficult tasks with teams of cooperating agents. Our interest in this area is motivated in part by our involvement in a Navy-sponsored micro air vehicle (MAV) project in which the goal is to solve difficult surveillance tasks using a large team of small inexpensive autonomous air vehicles rather than a few expensive piloted vehicles. Our approach to developing control programs for these MAVs is to use evolutionary computation techniques to evolve behavioral rule sets. In this paper we describe our architecture for achieving this, and we present some of our initial results.

• Type: Conference
• Year: 2000
• Where: ISMIS
• Authors: Jeffrey K. Bassett, Kenneth A. De Jong
• Keywords: Air Vehicles, Artificial Intelligence, Control Programs, Difficult Surveillance Tasks, ISMIS 2000