Tibor Bosse, Alexei Sharpanskykh, Jan Treur, Henk Blom, Sybert - - PowerPoint PPT Presentation

▶
tibor bosse alexei sharpanskykh jan treur henk blom
SMART_READER_LITE
LIVE PREVIEW

Tibor Bosse, Alexei Sharpanskykh, Jan Treur, Henk Blom, Sybert - - PowerPoint PPT Presentation

Dec 08, 2022 17 likes •257 views

Agent-Based Modelling of Hazards in ATM Tibor Bosse, Alexei Sharpanskykh, Jan Treur, Henk Blom, Sybert Stroeve SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012 vrije Universiteit amsterdam Contents 1. MAREA project and motivation 2.

slide-1
SLIDE 1

Agent-Based Modelling of Hazards in ATM

Tibor Bosse, Alexei Sharpanskykh, Jan Treur, Henk Blom, Sybert Stroeve

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012 vrije Universiteit amsterdam

slide-2
SLIDE 2

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

2

Contents

  • 1. MAREA project and motivation
  • 2. A generalised set of hazards in ATM
  • 3. Agent-based modelling of hazards for resilience analysis

–

TOPAZ model constructs

–

VU model constructs

–

New model constructs

  • 4. Analysis of results
  • 5. Conclusions and future research
slide-3
SLIDE 3

3

ATM: an Open Socio-Technical System

Complexity and performance variability in ATM

 Distributed human operators and technical systems  Considerable interconnectivity between the agents  Internal and external uncertainties and disturbances  Human role is important to cope efficiently with uncertainties

and disturbances

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

slide-4
SLIDE 4

4

Resilience Engineering

“Design of socio-technical systems that are able to resist a wide variety of demands, variations, degradations and disruptions” Human flexibility and system oversight are essential

 Away from error-thinking  Towards a broad view on human

performance in an overall system

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

slide-5
SLIDE 5

5

Mathematical Approach towards Resilience Engineering in ATM (MAREA)

Aim To develop a mathematical modelling and analysis approach that allows to bring Resilience Engineering at work for the complex ATM system Focus on human performance

Humans dealing with uncertainties and non-nominal conditions

Psychological and organisational models

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

slide-6
SLIDE 6

6

Identification of Hazards

Hazard = “Anything that may influence safety”

 Events / conditions / performance aspects  Humans / systems / environment  Interactions

NLR ATM Hazard Database

 ATM safety assessments  Hazard brainstorm sessions  4000+ hazards

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

slide-7
SLIDE 7

7

A Set of Generalised Hazards

4000+ Selection of unique hazards 525 Generalization of hazards Development (Set I) Validation (Set II) Wrong waypoints in database Transponder sends wrong call-sign False alert of an airborne system Track drop on controller HMI Pilot mixes up ATC clearances Pilot validates without checking Risk of a conflict is underestimated Alert causes attentional tunneling Controller has wrong SA about intent of aircraft Flight plans of ATC system and FMS differ Weather forecast is wrong Animals on the runway Resolution of conflict leads to other conflicts Contingency procedures have not been tested

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

slide-8
SLIDE 8

8

How to Model Hazards for Resilience Analysis?

Requirements

 Modelling at level of individual humans and technical systems  Possibility to capture complex non-linear dynamics  Availability of computational tools

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

Agent-based modelling

 ‘Agent’ = autonomous system interacting with environment  Agents represent behaviour at local level  Behaviour at global level ‘emerges’ in simulations

Human1 Human3 Human2 System1 System4 System2 System3 Accident Human6 Human4 Human5 System5

Organizational context

slide-9
SLIDE 9

Main Research Goal “To increase the percentage of potential hazards modelled by existing accident risk assessment methods for ATM” More specifically:

 Model hazards from ‘Set I’ via ABM approaches

Three Phases:

  • 1. TOPAZ model constructs (SID 2011)
  • 2. VU model constructs (ATOS 2012)
  • 3. New model constructs (SID 2012)

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

9

slide-10
SLIDE 10

TOPAZ Model Constructs

C1 Human Information Processing C8 Human Error C2 Multi-Agent Situation Awareness C9 Decision Making C3 Task Identification C10 System Mode C4 Task Scheduling C11 Dynamic Variability C5 Task Execution C12 Stochastic Variability C6 Cognitive Control Mode C13 Contextual Condition C7 Task Load

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

10

slide-11
SLIDE 11

Cognitive Control Mode (C6)

scrambled

  • pportunistic

tactical strategic subjectively available time degree

  • f

control error probability

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

TOPAZ Model Constructs - Example

11

slide-12
SLIDE 12

Matching Model Constructs to Hazards

  • Informal approach to assess ‘coverage’ of hazards
  • For each hazard-model combination perform ‘mental simulation’
  • Multiple analysts
  • Example: ‘Pilots do not react to controller call due to high workload’

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

12

scrambled call unimportant low priority no response

slide-13
SLIDE 13

13

Controller makes a reading error

 Human error  Multi-agent SA

Pilots do not react to controller call due to high workload

 Task identification  Task scheduling  Cognitive control mode

Failure of GPS system

 System mode

Pilot reports wrong position

 Human error  Multi-agent SA

Controller ignores an alert

 Multi-agent SA  ...

Procedure change  confusion

 Multi-agent SA  Decision making  ...

Cultural differences between airlines

 ...

Controller is fatigued and sleepy

 ...

Lack of experience in degraded modes

 ...

Covered Not Covered Partly 155 81 30

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

TOPAZ Model Constructs – Hazard Coverage

slide-14
SLIDE 14

VU Model Constructs

MC1 Bottom-up Attention MC7 Trust MC2 Experience-based Decision Making MC8 Formal Organisations MC3 Operator Functional State MC9 Learning MC4 Information Presentation MC10 Goal-oriented Attention MC5 Safety Culture MC11 Extended Mind MC6 Complex Beliefs in Situation Awareness

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

14

slide-15
SLIDE 15

Operator Functional State (MC3)

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

VU Model Constructs - Example

15 External World Operator

Task Demands Situational Aspects Task Execution State Task Demands Environment State Actions Recovery Effort Experienced Pressure Generated Effort Provided Effort Effort Motivation Task Goals Processing Expertise Profile Personality Profile Basic Cognitive Abilities Critical Point Maximal Effort Exhaustion

slide-16
SLIDE 16

Trust (MC7)

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

VU Model Constructs - Example

16

slide-17
SLIDE 17

17

Complex procedure causes R/T overload

 Operator Functional State  Formal Organisation

Controller has low confidence in validity of system alerts

 Trust

Controller is fatigued and sleepy

 Operator Functional State

Clutter of audio messages

 Information Presentation  Situation Awareness

Pilots falling asleep

 Operator Functional State  ...

Negotiation problems Pilot-ATC

 Trust  ...

A jolly atmosphere on the frequency

 ...

Icing of the wings

 ...

Aircraft picks up beacons with similar frequencies

 ...

Covered Not Partly

212 36 18

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

VU Model Constructs – Hazard Coverage

slide-18
SLIDE 18

New Model Constructs

A Unstabilised Approach H Merging or Splitting ATC Sectors B Handling of Inconsistent Information by a Technical System I Reduced Visibility C Sub-optimal Emotional Atmosphere J Weather Forecast Wrong D Complex or Unclear Procedures Leading to Confusion K Strong Turbulence E Changes in Procedures Leading to Confusion L Icing F Human Does Not Know When to Take Action M Influence of Many Agents on Flight Planning G Problems with Access Rights to an Information System N Uncontrolled Aircraft

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

18

slide-19
SLIDE 19

New Model Constructs - Example Emotion

S SR R R

qS qR SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

19

Sub-optimal Emotional Atmosphere (C)

slide-20
SLIDE 20

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

20

New Model Constructs - Example

Changes in Procedures Leading to Confusion (E)

slide-21
SLIDE 21

21

A jolly atmosphere on the frequency

 Operator Functional State  Emotion Contagion

Aircraft picks up beacons with similar frequencies

 Handling of Inconsistent Info

by a Technical System Icing of the Wings

 Icing

Unstabilised Approach

 Approach

Strong variation in view

 Weather  ...

Standard R/T not adhered to

 Confusion  ...

Security Intrusion

 ...

Unmanned Arial Vehicles

 ...

Military Aircraft Shoots a Civil Aircraft Down

 ...

Covered Not Partly

244 6 16

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

New Model Constructs – Hazard Coverage

slide-22
SLIDE 22

Conclusion 38 agent-based model constructs have been identified

  • 13 TOPAZ model constructs
  • 11 VU model constructs
  • 14 new model constructs

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

22 Covered Not Partly

244 6 16

Result: considerable improvement of hazard coverage

Covered Not Covered Partly 155 81 30

Covered Not Partly

212 36 18

+ VU TOPAZ + NEW

slide-23
SLIDE 23

Next Steps

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

23

  • Integration of model constructs
  • Formalisation of integrated model constructs
  • Preliminary ‘validation’ of model constructs
  • Test the coverage of Hazard Set II using ‘mental simulation’
  • Apply model constructs to safety-relevant scenarios
  • Validate scenarios using interviews with operational experts

Human1 Human3 Human2 System1 System4 System2 System3 Accident Human6 Human4 Human5 System5

Organizational context

slide-24
SLIDE 24

24

SESAR Innovation Days, Braunschweig, Germany, Nov 27-29, 2012

Questions