A Case for Theory-Based Research on Level of Automation and

Adaptive Automation

David B. Kaber

Department of Industrial Engineering

North Carolina State University

This work was supported by a grant through the National Aeronautics and Space Administration Langley Research Center. The opinions expressed are those of the author and do not necessarily reflect the views of NASA.

Thoughts on the “what”, “how” and “when” of automation:

“What?”

“How?”

“When?”Monitoring

Generating

Selecting

Implementing

Human

Human/Machine

Machine

Stages of Information Processing

Level of Automation (LOA)

Adapted from Endsley (1997).

TnT0

Adaptive Automation (AA)

Information Processing and Automation Design Theory:

Qualitative models relating stages of information processing to automation design.– Parasuraman et al. (2000) – Model of types and

levels of automation.

A model of types and levels of automation (from Parasuraman et al. (2000)).

Information Acquisition

Information Analysis

Decision-making

Action

Degree of Automation

High

Low

High

Low

High

Low

High

Low

Qualitative Models of Human-Automation Interaction (cont.):

Endsley & Kaber (1999):– Four-stage model of information processing.– Used to define levels of automation or function

allocation schemes.

Monitoring Generating Selecting Implementing

Human

Human/Machine

Machine

Level of Automation

Human

Human/Machine

Machine

Human

Human/Machine

Machine

Human

Human/Machine

MachineAdapted from Endsley and Kaber’s (1999) taxonomy of levels of automation.

Taxonomy of Levels of Automation:

Endsley & Kaber (1999):– 10 discrete levels of automation (LOAs) in complex

systems control. Roles

Level of Automation MONITORING GENERATING SELECTING IMPLEMENTING

(1) MANUAL CONTROL Human Human Human Human

(2) ACTION SUPPORT Human/Computer Human Human Human/Computer

(3) BATCH PROCESSING Human/Computer Human Human Computer

(4) SHARED CONTROL Human/Computer Human/Computer Human Human/Computer

(5) DECISION SUPPORT Human/Computer Human/Computer Human Computer

(6) BLENDED DECISION MAKING Human/Computer Human/Computer Human/Computer Computer

(7) RIGID SYSTEM Human/Computer Computer Human Computer

(8) AUTOMATED DECISION MAKING Human/Computer Human/Computer Computer Computer

(9) SUPERVISORY CONTROL Human/Computer Computer Computer Computer

(10) FULL AUTOMATION Computer Computer Computer Computer

Bases for Qualitative Models:

Common models of HUMAN information processing (HIP).– Sanders & McCormick (1993) – Multi-stage model

identifying functions of humans and machines.

Adapted from Sanders and McCormick’s (1993) model of human and machine functions in human-machine systems.

Sensing - Perception

Information Processing

Decision-making

Action Functions

Feedback

Automaticity

Another Basis for Current Models:

Wickens (1992) – Model of HIP influential in Parasuraman et al. (2000) model of types and LOAs.

Perception Decision-making

Response Execution

Working Memory

Long-term Memory

Short-term Sensory Store

FeedbackModel of human information processing (adapted from Wickens (1992)).

Applying Information Processing Models to Automation Design:

– Present ways of classifying functions of human-machine systems.

Note: Entire process is similar to Wickens (1992) approach to systems design.

Mission Analysis

Function Analysis

Task Analysis

Basic System

Concept

Initial Function

Allocation

Dynamic Function

Allocation

Determine “what” to automate?

Determine “how” to automate?

Determine “when” to automate?

Parasuraman et al. (2000) and Endsley & Kaber (1999) models:

Unique Aspects of Approach:

Link LOAs to stages of information processing.

Types of functions considered are general information processing functions.

– Compare with Sheridan & Verplank’s (1978) hierarchy of LOAs – Functions primarily represent action states (“gets”, “starts”, etc.) or choice reactions (“selects”).

Historical function allocation lists technology-centered (Fitts, 1956):

– Advances in understanding of out-of-the (control) loop performance (Endsley & Kiris, 1995; Kaber et al., 1998) shifted focus of lists on human abilities.

– Contemporary taxonomies of LOA (e.g., Endsley & Kaber (1999)) developed by considering performance consequences of automation

Complacency, vigilance decrements, loss of SA, skill decay.

Importance of Research: Link general theories on information processing and

automation to applications.– Parasuraman et al. (2000) - Application of model to air traffic control

(ATC).

Contemporary models of human-automation interaction (HAI) may serve as design rationale for human-centered automation.– Knowledge of performance with systems can be classified according

to model and used to further develop general theory on HAI. Theory may serve to answer several questions:

– What stages of information processing are conducive to automation from human perspective?

– To what extent can stage be automated safely/effectively?– What stages are robust to automation reliability problems?

Theoretical Research Challenges:

Validate processing stages of models in terms of representation of actual functions performed by human-machine systems.

Models can be used for logical function allocation, but practical implications are difficult to predict.– Endsley & Kaber (1999):

Intermediate LOAs moderate workload, maintain situation awareness (SA) and improve performance.

Low level automation produced superior performance, but at cost of SA.

“Good” SA observed under high LOAs.

Hypothesis

Results

Example Model Application:

Applied Endsley & Kaber’s taxonomy of LOAs to advanced commercial aircraft (MD-11) (Tan et al., 2000):– Conducted complex systems

analysis.– Developed high-fidelity

simulation for evaluation of existing system automation.

– Categorized actual modes of automation according to taxonomy.

Flight Control Panel (FCP)Out-of-Cockpit

View

Flight Mode Annunciator (FMA)

Primary Flight

Display (PFD)Navigation Display

(ND)Multi-functionalControl DisplayUnit (MCDU)

Throttle/attitude control

Flight Simulator Displays:

Roles

Level of Automation MONITORING GENERATING SELECTING IMPLEMENTING

(1) MANUAL CONTROL Human Human Human Human

(2) ACTION SUPPORT Human/Computer Human Human Human/Computer

(3) BATCH PROCESSING Human/Computer Human Human Computer

(4) SHARED CONTROL Human/Computer Human/Computer Human Human/Computer

(5) DECISION SUPPORT Human/Computer Human/Computer Human Computer

(6) BLENDED DECISION MAKING Human/Computer Human/Computer Human/Computer Computer

(7) RIGID SYSTEM Human/Computer Computer Human Computer

(8) AUTOMATED DECISION MAKING Human/Computer Human/Computer Computer Computer

(9) SUPERVISORY CONTROL Human/Computer Computer Computer Computer

(10) FULL AUTOMATION Computer Computer Computer Computer

Defining Aircraft Automation in Terms of Taxonomy of LOA:

Taxonomy and example LOA description:

Control Responsibilities Level of Automation

Subsystems Pilot Automation PFD -monitor flight parameters -monitor flight parameters ND -monitor lateral flight plan -monitor lateral flight plan Throttle/ Attitude Control

MCDU -generate/select (explicitly)/ program flight plan manually -edit/re-program (corrective actions) flight plan manually

-implement flight plan selected by pilot -implement flight plan edited by pilot -provide warnings to pilot for corrective actions

FCP

Batch Processing

FMA -monitor modes of operation -display current system status

2Study AA by

considering many points along

continuum of automation.

1

Research to Enhance Basic Understanding of

Automation: Adaptive automation (AA) (or

“when” to automate) primarily studied using laboratory simulations of complex systems.

Majority of AA research investigates concept from binary perspective.– Automation is continuous variable

made discrete for research purposes.

Automation

Full Automation

Manual Control “When?”TnT0

Decision Support

Batch Processing

Decision Support

Supervisory Control

Supervisory Control Manual

Control

Action Support

Binary ApproachContinuous Approach

Study AA in high-fidelity

simulations, or in context.

3

4

5

Considering Models of HAI in Adaptive

Systems Research: Focus of AA research has been on

early stages of information processing:– Do not have understanding of effect of AA

on decision-making and planning.– People may not adapt well to dynamic

allocations of decision functions. Research has not systematically

examined performance and SA effects of dynamic function allocations (DFAs) across stages of information processing.

Study impact of AA on latter

stages of information processing.

Study SA and performance effects of AA

across functions represented in models of HAI.

Study impact of AA applied

exclusively to each stage of information processing.

Parasuraman et al. (1993)

7Consider need to

keep-track of LOAs in AA

studies.

6Compare voluntary,

involuntary and shared DFA management

Final Issues:

– Computer authority shown to reduce excessive cyclings between control modes (Hilburn et al., 1993).

– Automation directed DFAs improve human manual control – operator does not need to evaluate who should be doing what (Kaber & Riley, 1999).

Approach AA research cautiously - complexity of system design may cause mode awareness problems (Sarter & Woods, 1995).

Need to determine who (human or automation) should have authority over DFAs:

Conclusions:

Can’t study AA without considering LOA – concepts “like peas and carrots” (F. Gump).

Taxonomies of LOA provide means for systematically studying complexities of automation.

Need to apply models of HAI to real systems to expand theory to design rationales.– Classify systems and understand underlying factors in

automated system performance.

Need to develop framework of AA research.