Recent Developments in Task Modelling

1. Technical University of Crete Dept. of Production Engineering & Management, Crete, Greece Recent Developments in Task Modelling Tom Kontogiannis

2. Operational Problems Giving Rise to Hazards & Incidents in Process Industries • Unanticipated events that require modifications in the procedures & methods required to do the job • High workload experienced by human operators • Changing the allocation of tasks and taking over new responsibilities without adequate practice • Coping with interruptions and suspended tasks • Changing plans & priorities under time pressure • Recovering erroneous actions that occurred

3. Use of Task Analysis Methodologies in the Control of Major Hazards • Task analysis methodologies are used to examine: • Goals and plans utilized by operators to perform tasks • The allocation of tasks between different operators • Critical task information obtained from control panels and operating procedures • They can give an indication of operator workload • They provide input to human error analysis • Their output is used to optimize procedures, job methods, control panel design, and training

4. Task Analysis Methodologies • Hierarchical Task Analysis • Operational Sequence Diagrams • Link Analysis • Time Line Analysis • Cognitive Task Analysis (Ainsworh & Kirwan, 1990)

5. An example of Hierarchical Task Analysis PLAN 1. At all times assess situation. Assign staff & tools first and continue with plan implementation Perform task PLAN 1 PLAN 4. Select goals according to the planned sequence of goals of the assessment process Assess situation & decide goal sequence Assign staff & tools Implement/Plan sequence of goals PLAN 5. Do T6 then T7 then do T89 & T1011 together If workload is high abort task or assign to other person PLAN2 (assess situation). Start with goal 3. If temp=high, do goal 1 aborting rest. If temp=medium, do goal 3 providing goal 1 is not in progress. Finish with goal 2 PLAN 7. Do all in sequence. If workload is high then abort task or assign to other person PLAN 3 Do in sequence PLAN 8 & 10. Do in parallel. If workload is high do in serial or assign task to other person “Assess situation” INPUTS “Process goals” Attend to pending tasks Process goals Release staff & tools PLAN 9. Choose task with lower workload (i.e., due to error or high workload) PLAN 4 Perform goal 1 Perform goal 2 Perform goal 3 Plan 5 PLAN 6 Do in sequence PLAN 7 Task 6 Task 7 Task 89 Task 1011 Task 1 Task 2 Task 3 Task 45 PLAN 8 PLAN 9 PLAN 10 Task 8 Task 9 Task 10 Task 11 Task 12 Task 13 Task 4 Task 5

6. Inadequacies of Task Analysis • Task Analysis describes “how the job should be done” under a set of well-defined job conditions • T.A. runs into difficulties in cases where system events require modifications in goal priories, task sequences, and allocation of tasks • It is difficult to model unanticipated events resulting in interruptions and suspended tasks • T.A. cannot model adequately the workload encountered by operators

7. From Task Analysisto Task Modelling • Task modelling is a new set of methodologies that provide computer simulations of human tasks • The output of task analysis (e.g., task sequences) can be used in specifying a task network • A task model represents the control of tasks, the flow of information, & the utilization of resources • Software tools can be used to verify the task model under a wide range of conditions (e.g., stochastic duration, external events, human errors)

8. Task Modelling Techniques • GOMS (John & Kieras, 1996) • MicroSAINT (Laughery & Corker, 1997) • ConcurTaskTrees (Paterno et al, 1997) • TOBOLA (Uhr, 2002) • DIANE+ (Tarby & Barthet, 1996)

9. Task accuracy Task duration (prob. distribution) Task release condition Beginning effect Ending effect Primary/contingent operators Task priorities Task loadings Probability of failure Failure consequences in time and accuracy Branching conditions (e.g., probabilistic, tactical, multiple) Data Requirements for Specifying Task Networks

10. Output of Task Modelling • A time line of completed tasks • A record of interrupted, resumed or failed tasks • The allocation of tasks to operators • An estimate of operator workload (e.g., analogous to the number of tasks carried out in parallel) • Performance measures (e.g., speed & accuracy) • An evaluation of the consequences of task failures

11. Inadequacies of Task Modelling • Utility problems • Exhaustive data requirements • Expert estimates on data (e.g., task loadings) • Expertise in task modelling languages and codes • Functional problems • The lack of a USER MODEL to indicate how operators process task information and re-schedule tasks • The lack of modelling several diagnostic and decision-making activities

12. A New Approach to Task Modelling based on Petri Nets • TASK MODEL • Petri Net graphs are used to build the task network • A taxonomy of 15 types of task sequences (plans) • Task templates regulate information to the User Model • USER MODEL (expressed in mathematical terms) • Recall-Forget model, Task Selection model, Operator Assignment model, Human Error Execution model • DIAGNOSIS & DECISION MAKING

13. MONITORING & DIAGNOSIS DECISION MAKING TASK MODEL Goal diagram Task diagram USER MODEL Recall tasks Select tasks Assign roles Execute tasks A New Approach to Task Modelling TASK TEMPLATE

14. A Triple Representation of Task Networks • The Petri Net Graph helps to visualize task relationships and interactions • The Mathematical modelling of the network helps the application of formal analysis techniques • The code segments of transitions helps the programming of high-level functions and routines (e.g., implementation of user models)

15. A Coloured Petri Net (CPN) Representation of Task Networks • A Net graph consisting of nodes and arcs • Placeshold information about resources, releasing conditions, beginning and ending effects • Transitionsrepresent tasks that manipulate the information held in adjacent places • Tokens are ‘data items’ activating places as they move around them • Arcs are inscribed with expressions (e.g., functions, variables)

16. Mathematical notation of Petri Nets • A Petri Net is a birartite directed graph • G = [ P, T, A ] where: • P is a set of places • T is a set of transitions • A is a set of directed arcs • The state of the system is represented as a matrix of marked places and enabled transitions • Formal analysis techniques can be used on the task network

17. Start Goal 3 Goal 1 Goal 2 The Task Model as a Petri Net Graph T1 T2 T7 T8 T12 T13 T3 T9 T14 T6 T11 T15 End

18. A A B T B DISCRETIONARY PLANS SEQUENTIAL PLANS Discretionary inclusive plans: Do both A and B, in any order, or concurrently Fixed sequences: Do A and B in specified order Prioritized sequences: Do both A and B giving priority to A A Discretionary exclusive plans: Do either A or B, in any order secondary place A secondary place B B Unordered sequences: Do both A and B in any order, but not concurrently A Taxonomy of Plans A Optional plans: Do either A or B, or both A T secondary place B [guard] [* Guard is to prevent transition T from firing twice] B N out of M joins: Do only n out of m tasks Interleaved sequences: Start B before A is completed pause T1 A A2 A1 T2 B @ + delay secondary place T3 C B

19. A Task Template for passing Information between Task and User Models Task Tin_2 t (g,id) Int Task_ID [t=id] t t g USER MODEL Data Data Super_in Super_out Tin_1 BEGIN END [t=t_out] T_out g If s<>Done & S <> Started then 1`(g,t) else empty If pass=on then 1`g else empty (g,turn,t_out,imp) i+1 i tsl tsl TaskStateList s1 s1 1`Default State s2 AllStates Counter s2 State If s<>Done then 1`g else empty

20. The User Model Super_in Super_mid Super_out ATTEND TASKS Selected tasks PERFORM TASKS (g,t) (g,t,s) (g,t,s) (g,t,s,imp) stm stm STM Tasks Tasks to be recalled (g,t) stm^^[(g,t)]

21. The Human Memory and Selective Attention Models RecDataList TaskStateList PrioDataList rdl pdl tsl1 tsl1 pdl rdl tsl2 tsl2 Super_in Super_in Super_mid RECALL TASKS SELECT TASKS (g,t) (g,t,s1) (g_out, t_out, Started) If s<>Default then 1`(g,t,s,p,a,dur) else empty @+ delay1 If pass=on then 1`(g,t) else empty msl1 ShedConsList BodyList ManStateList msl1 scl bl msl2 msl2 scl bl STM stm1 stm1 stm2 stm2

22. Operator Assignment, Workload & Human Error FailConsList fcl fcl fcl fcl TaskStateList tsl1 tsl1 tsl2 tsl2 ManStateList msl1 msl1 msl2 msl2 Super_out (g,turn,t,s2,imp) Super_mid TaskXPerson Active task list ASSIGN ROLES EXECUTE TASKS (g,t,s1) If s<>Default then 1`(g,t,s,p,a,dur) else empty @+ delay1 (g,t,s,p,a,dur) Task (g,t, Default) If s<>Done and turn=on then 1`(g,t) else empty TaskCharList tcl tcl (g,t) tcl Task tcl ManCharList Do in serial mcl mcl If s=Default then 1`(g,t) else empty @+delay2 mcl mcl ConstraintList cl cl cl cl

23. Declaration of Colours (1/2) • TaskCharList contains task loadings and conflicts on three resource channels (i.e., perception-choice-action) • TaskStateList contains an updated record of starting and finishing times, assigned operators, status of tasks, priorities of tasks, and state of goals (switches) • ManCharList contains task information about durations and accuracies for different operators for each task • ManStateList contains an updated record of the workload and availability of operators

24. Declaration of Colours (2/2) • RecDataList contains data about cost of forgetting, and strength of reminding cues for each task • PrioDataList contains data about cost of task interruption, deadlines to avoid consequences and cost of consequences incurred • ConstraintList contains data about the allocation rules that should be applied to each task • FailConsList contains data about side effects to tasks upstream or downstream a specific task

25. n R = P (A/D) Cj j=1 A Model of Operator Assignment Select operators with assignment scores above a limiting condition R: Assignment score for a specific operator P: priority of a specific task A: task accuracy for an operator D: task duration for an operator j= 1 … n, rules for task allocation Cj: operator compliance with each allocation rule

26. A Model of Operator Workload (Multiple Resource Theory) n m n-1 n WT = t=1 ( Lt ) + i=1cit=1 s=t+1 (a t,i +a s,i ) WT: instaneous workload at time T i= 1 … m are the resource channels (e.g., perception) t,s= 1 … n are the operator tasks Lt: standard loading for task t a t,i : loading on channel i to perform task t a s,i : loading on channel i to perform task s c i = conflict between tasks sharing channel i

27. Strategies for Workload Management • A penalty is incurred in terms of accuracy & speed • Do not begin next task (Defer or skip next task) • Execute tasks sequentially instead of parallel • Interrupt ongoing task in favour of new (Replace) • Reassign ongoing task to another operator • Allocate new task to a less busy operator

28. A Model of Execution Errors(Errors are related to high workload) • No error impact – next tasks is attended • Task is mistimed (premature or delayed start) • Next task cannot be started • Side effects are incurred, having an impact on previously completed tasks • Immediate error impact in terms of a penalty in task duration, accuracy and task loading • Error impact occurs when unsuccessful task is repeated (e.g., operator assignment may change) • Commission errors are modeled as separate task networks

29. Output of the Methodology • A time line of completed tasks • A graph showing operator workload at all times • A record of tasks forgotten due to system changes • A record of interrupted, resumed, repeated tasks • A record of changes in goal priorities • A record of operator assignment scores for tasks • Performance measures (e.g., speed & accuracy) • An evaluation of human error consequences

30. Advantages of the Methodology • Takes into account the CONTEXT OF TASKS (e.g., current workload, interrupted tasks) • Workload takes into account mental activities (e.g., diagnosis) in addition to task loadings • Different USER MODELS can be tested (e.g., memory model, selective attention model) • The relationship between high workload and human error is better explored • Diagnosis and task planning are integrated

31. Disadvantages of the Methodology • Exhaustive data requirements, especially with the addition of user models • Alternative plans must be thought of and specified in advance • Commission errors are modeled as separate task networks and this may clutter the diagrams • Reliance on availability of human error probabilities

32. Future Developments • Verification of human memory and selective attention models in the context of a real scenario • Development of a model for diagnosis and decision making • Development of an architecture for ‘abstract’ planning to be integrated with the task network