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1 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Kip E. Johnson, MIT Dept. of Aero & Astro Engineering Advisors Jim Kuchar.

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Presentation on theme: "1 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Kip E. Johnson, MIT Dept. of Aero & Astro Engineering Advisors Jim Kuchar."— Presentation transcript:

1 1 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Kip E. Johnson, MIT Dept. of Aero & Astro Engineering Advisors Jim Kuchar & Chuck Oman Sponsored by Office of Naval Research JUP Conference, 21-22 June 2001 Hosted by Ohio University Sponsored by NASA & FAA

2 2 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” OVERVIEW:  Problem Identification  Experimental Design

3 3 MOTIVATION:  Decision-Making in Complex Environments  Replanning Under Time Pressures PROBLEM UNIQUENESS:  Automation will Never “See” Everything  Difficulty in Quantifying a Solution’s Value  Unstructured Aspects to Problem  Multiple Competing Interests & Goals for Solution “Experimental Study of Automation to Support Time-Critical Replanning Decisions”

4 4 Human-Automation Interaction in a Decision-Support Task Adopted from J. Kuchar

5 5 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Example Applications: Military Combat Aviation Medicine Chemical and Energy Production Finances  Focus on “Real-Time In-FlightReplanning”  Route Replanning

6 6 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Replanning Task Characteristics Threats/Hazards Time on Target Fuel Constraint Information Integration Solution Information Elements HA

7 7 QUESTION? To What Degree should the Replanner Automation Filter and Integrate Information? Take Advantage of Human’s Intuition and Ability to Integrate Diverse and Complex Information. Replanner Should Have “SMART” Automation Logic Based on Task and Time Pressures. HYPOTHESIS: As Time Pressure Decreases, More Integrated Automation Support May Hinder Pilot Performance. “Experimental Study of Automation to Support Time-Critical Replanning Decisions”

8 8 Task Timescale Level of Information Integration in Automation Subsecond SecondMinute Hour Low High Information Overload Good Observability Time Available to Assimilate Support with Other Info Opaque Support Cannot Assimilate Automated Guidance w/ Other Info Timely, Specific Solution (Must Use Correct Info) Generalized System Design Notional Hypothesis of Interaction Between Information Integration & Task Timescale

9 9 RESEARCH GOALS: 1. Find a Quantifiable Relationship Between: Time Pressures Degree of Information Integration in Automation Resulting Decision Performance 2. Build a Generalized Model of Decision Support Automation. 3. Identify Information Support Needs of Human “Experimental Study of Automation to Support Time-Critical Replanning Decisions”

10 10 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” EXPERIMENTAL DESIGN: Replanning Protocol: View Preplanned Mission Change in Environment Hazard, Time on Target, or Fuel Update Route Suggested with Varying Levels of Information Integration Subject Modifies Flight Plan Under Time Pressure Minimize Threat Exposure and Time on Target Deviation Meet Time on Target and Fuel Constraints

11 11 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Sequence of Events Time Pressure View Scene Unlimited Replanning Auto-Route Suggestion Mission Change: Hazard,ToT,Fuel Time Replanning Performance Data Recorded

12 12 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Pre-Planned Mission Constraint Gauges Original Route Hazards Start Target Must-Fly Finish

13 13 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Updated Mission Plan Suggested Route Constraint Gauges Alerts Time Pressure

14 14 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” COST FUNCTION: Cost Route = Cost Hazard + Cost ToT Fuel = Constraint

15 15 Inherent Route Cost Time Pressured Route Difference No Pressure Route Difference “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Dependent Variable: Time Pressure

16 16 INDEPENDENT VARIABLES: Time Pressures (TBD): “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Area of Interest Time Pressure Performance T1T1 T2T2 T3T3

17 17 INDEPENDENT VARIABLES: Information Elements Integrated by Automation  No Automation, Manual Replan  Original Route Remains  Constraint Information Filtration Only  Route Modified to Optimize Time on Target Deviation & Satisfice Fuel Constraint  Threat Information Filtration Only  Route Modified to Avoid/Minimize Hazard Levels  Integration of Constraint + Threat Field Information  Route Minimizes Threat Exposure + Satifices Time on Target and Fuel Constraints “Experimental Study of Automation to Support Time-Critical Replanning Decisions”

18 18 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Optimize ToT, Meet Fuel Constraint

19 19 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Minimize Threat Exposure

20 20 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Satisfice Constraints + Minimize Threat Exposure

21 21 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Experimental Test Matrix Counterbalanced Scenarios of Similar Complexity Repeated-Measures Analysis of Variance

22 22 “Experimental Study of Automation to Support Time-Critical Replanning Decisions” Status :  In-Flight Replanner Software Developed  Generating Scenarios for Pilot Experiment Future:  Run Data Collection w/ Subjects  Refine Pilot Experiment for Formal Study

23 23 “Experimental Study of Automation to Support Time-Critical Replanning Decisions”

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