Building and Improving Tactical Agents in Real Time through a Haptic-Based Interface

Abstract

This article describes and evaluates an approach to create and/or improve tactical agents through direct human interaction in real time through a force-feedback haptic device. This concept takes advantage of a force-feedback joystick to enhance motor skill and decision-making transfer from the human to the agent in real time. Haptic devices have been shown to have high bandwidth and sensitivity. Experiments are described for this new approach, named Instructional Learning. It is used both as a way to build agents from scratch as well as to improve and/or correct agents built through other means. The approach is evaluated through experiments that involve three applications of increasing complexity – chasing a fleer (Chaser), shepherding a flock of sheep into a pen (Sheep), and driving a virtual automobile (Car) through a simulated road network. The results indicate that in some instances, instructional learning can successfully create agents under some circumstances. However, instructional learning failed to build and/or improve agents in other instances. The Instructional Learning approach, the experiments, and their results are described and extensively discussed.

Keywords

• haptics
• instructional learning
• multimodal machine learning
• tactical agents