Engineering topics

Hill, Randall W. Jr.

Publications and source records attributed to Hill, Randall W. Jr..

3 records

NASA NTRS1993

Toward an Embedded Training Tool for Deep Space Network Operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor.

Johnson, W. Lewis↗

NASA NTRS1993

Impasse-Driven Tutoring for Reactive Skill Acquisition

We are interested in developing effective performance-oriented training for the operation of systems that are used for monitor and control purposes. We have focused on one such system, the communications Link Monitor and Control (LMC) system used in NASA's Deep Space Network (DSN), which is a worldwide system for navigating, tracking and communicating with unmanned interplanetary spacecraft. The tasks in this domain are procedural in nature and require reactive, goal-oriented skills; we have previously described a cognitive model for problem solving that accounts for both novice and expert levels of behavior as well as how skill is acquired [Hill and Johnson, 1993]. Our cognitive modeling work in this task domain led us to make a number of predictions about tutoring that have influenced the design of the system described in this paper.

Johnson, W. Lewis↗

NASA NTRS1993

Impasse-Driven Tutoring for Reactive Skill Acquisition

We introduce a new approach to intelligent tutoring in performance-oriented training environments based on a method called situated plan attribution. The aim of this method is to provide contextualized tutoring for procedural tasks requiring reactive, goal-oriented skills. We use the term plan attribution instead of plan recognition because it does not assume that the problem solver is consciously executing plan. We avoid some of the pitfalls of other popularly used methods, i.e., model tracing and procedure net grammars, by selectively using an expert cognitive model to generate advice after detecting a problem solving impasse. The tutor attributes a set of plans to the student based on a task description. Each action is evaluated with respect to: the student's attributed plans, its actual effects on the training device, and the contextualized goals associated with the plans.

Johnson, W. Lewis↗