Using a Genetic Algorithm to Learn Behaviors for Autonomous Vehicles,
Abstract
Truly autonomous vehicles will require both projective planning and reactive components in order to perform robustly. Projective components are needed for long-term planning and replanning where explicit reasoning about future states is required. Reactive components allow the system to always have some action available in real-time, and themselves can exhibit robust behavior, but lack the ability to explicitly reason about future states over a long time period. This work addresses the problem of creating reactive components for autonomous vehicles. Creating reactive behaviors (stimulus-response rules) is generally difficult, requiring the acquisition of much knowledge from domain experts, a problem referred to as the knowledge acquisition bottleneck. SAMUEL is a system that learns reactive behaviors for autonomous agents. SAMUEL learns these behaviors under simulation, automating the process of creating stimulus-response rules and therefore reducing the bottleneck. (AN)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 12, 1992
- Accession Number
- ADA294103
Entities
People
- Alan C. Schultz
- John J. Grefenstette