《人类监控自动化系统》英文版 Function allocation and Task Analysis

16.422 Human Supervisory Control Function allocation and Task Analysis Massachusetts Institute of Technology

16.422 Human Supervisory Control Function Allocation and Task Analysis

Human Systems Engineering 16.422 sIon” System/Software Reqs User survey. need's analysys, etc Feasiowty assessent Artifact ad Mwe-systemm evalation 户 rbm arce and usab reqs Innovative comeas for aKHE labod'uodgset planmin next versio? release Installation System/Software Preliminary Design Acquisition Cycle Storyboards and dew on statons Puwe如 GNc體 aterials U design stardara's Integration Test Detailed Design an7mo如 Desig tradea and wodoowanalysis Devekbo training M aterials Unit Development Oline help and documen?吉a Define perfom ance ard effect veness criteria Usablity evaluation df prckaypes Planning→ Analysis-丶 Detail design→Test& Evaluation

Human Systems Engineering 16.422 Planning →Analysis → Detail Design → Test & Evaluation

Functions tasks 16.422 Planning Mission Scenario analysis Analysis Function analysis Function Allocation Task Design Test Analysis Eⅴ aluation System Design

Functions & Tasks 16.422 Planning Mission & Scenario Analysis Function Analysis Function Allocation Task Analysis System Design Analysis Design Test & Evaluation

Fitts list 16.422 Attribute Machine Human bee Superior Comparatively slow Power Superior in level in consistency Comparatively weak Out p Consistency Ideal for consistent, repetitive action Unreliable, learning& fatigue a fa actor Information Multi-channel Primarily single channel Capacity Memory Ideal for literal reproduction, access Better for principles& strategies restricted and formal access versatile innovative Reasoning Deductive, tedious to program, fast Inductive, easier to program, slow, Computation& accurate, poor error correction accurate, good error correction Sensing Good at quantitative assessment, Wide ranges, multi-function poor at pattern recognition udgment Ju Perceiving Copes with variation poorly Copes with variation better, susceptible to noise susceptible to noise Hollnagel, 2000 inductive and deductive Induction is usually described as moving from the specific to the general, while deduction begins with the general and ends with the specific, arguments based on experience or observation are best expressed inductively, while arguments based on laws, rules, or other widely accepted principles are best expressed deductively

Fitts’ List 16.422 Attribute Machine Human Speed Superior Comparatively slow Power Output Superior in level in consistency Comparatively weak Consistency Ideal for consistent, repetitive action Unreliable, learning & fatigue a factor Information Capacity Multi-channel Primarily single channel Memory Ideal for literal reproduction, access restricted and formal Better for principles & strategies, access versatile & innovative Reasoning Computation Deductive, tedious to program, fast & accurate, poor error correction Inductive, easier to program, slow, accurate, good error correction Sensing Good at quantitative assessment, poor at pattern recognition Wide ranges, multi-function, judgment Perceiving Copes with variation poorly, susceptible to noise Copes with variation better, susceptible to noise Hollnagel, 2000 inductive and deductive. Induction is usually described as moving from the specific to the general, while deduction begins with the general and ends with the specific; arguments based on experience or observation are best expressed inductively, while arguments based on laws, rules, or other widely accepted principles are best expressed deductively

Some problems with Fitts 16.422 Tasks/functions defined in machine terms. not human-oriented Introduces a bias Laws of human behavior Environmental/ecologic context Learning, fatigue, stress, anxiety generally not incorporated into design picture Task division vs task complement Static vs dynamic allocation Adaptive allocation/automation Function allocation is not binary Bandwidth Trust Machine/computer metaphors

Some problems with Fitts… • Tasks/functions defined in machine terms, not human-oriented – Introduces a bias – “Laws of human behavior” • Environmental/ecologic context • Learning, fatigue, stress, anxiety generally not incorporated into design picture • Task division vs. task complement • Static vs. dynamic allocation – Adaptive allocation/automation – Function allocation is not binary 16.422 • Bandwidth • Trust • Machine/computer metaphors

Designing automation to support Information processing 16.422 human Perception Sensor Decision R response Processing Working Selection Memory Making Automation Decision Information Information Action action cquisition Analysis Implementation Selection *Parasuraman Sheridan Wickens. 2000

Designing automation to support information processing 16.422 Human Sensory Processing Response Selection Decision Making Perception/ Working Memory Information Acquisition Action Implementation Decision & Action Selection Information Analysis Automation *Parasuraman, Sheridan, Wickens, 2000

What should be automated? Identify types of automation InformationInformation Action Acquisition Analysis action Implementation A Model of Identify levels of automation Types and Low(man High(full automation) Levels of Apply primary evaluative criteria Human Performance Consequences Automation a Mental workload · Situation awareness Complacency Skill degradation Initial types levels of automation Apply secondary evaluative criteria Automation reliability Final types levels of automation Parasuraman, Costs of action outcomes Sheridan, Wickens. 2000

A Model of Types and Levels of Automation* Information Acquisition Action Implementation Decision & Action Selection Information Analysis What should be automated? Identify types of automation Identify levels of automation Apply primary evaluative criteria: Human Performance Consequences • Mental workload • Situation awareness • Complacency • Skill degradation Initial types & levels of automation Final types & levels of automation Apply secondary evaluative criteria: • Automation reliability • Costs of action outcomes Low (manual) High (full automation) *Parasuraman, Sheridan, Wickens, 2000

Sheridan and Verplank's 10 Levels of Automation of decision and action selection 16.422 Automation Automation Description Level The computer offers no assistance human must take all decision and actions The computer offers a complete set of decision/action alternatives, or narrows the selection down to a few or 23456789 suggests one alternative, and executes that suggestion if the human approves, or lows the human a restricted time to veto before automatic execution, or executes automatically, then necessarily informs humans, and informs the human only if asked,or informs the human only if it, the computer, decides to 10 The computer decides everything and acts autonomously, ignoring the human

Sheridan and Verplank’s 10 Levels of Automation of Decision and Action Selection 16.422 Automation Level Automation Description 1 The computer offers no assistance: human must take all decision and actions. 2 The computer offers a complete set of decision/action alternatives, or 3 narrows the selection down to a few, or 4 suggests one alternative, and 5 executes that suggestion if the human approves, or 6 allows the human a restricted time to veto before automatic execution, or 7 executes automatically, then necessarily informs humans, and 8 informs the human only if asked, or 9 informs the human only if it, the computer, decides to. 10 The computer decides everything and acts autonomously, ignoring the human

What should be automated? Identify types of automation InformationInformation Action Acquisition Analysis action Implementation A Model of Identify levels of automation Types and Low(man High(full automation) Levels of Apply primary evaluative criteria Human Performance Consequences Automation a Mental workload · Situation awareness Complacency Skill degradation Initial types levels of automation Apply secondary evaluative criteria Automation reliability Final types levels of automation Parasuraman, Costs of action outcomes Sheridan, Wickens. 2000

Information Acquisition Action Implementation Decision & Action Selection Information Analysis What should be automated? Identify types of automation Identify levels of automation Apply primary evaluative criteria: Human Performance Consequences • Mental workload • Situation awareness • Complacency • Skill degradation Initial types & levels of automation Final types & levels of automation Apply secondary evaluative criteria: • Automation reliability • Costs of action outcomes Low (manual) High (full automation) A Model of Types and Levels of Automation* *Parasuraman, Sheridan, Wickens, 2000

Function allocation criteria 16.42 1 No difference in the relative capabilities of human machine 2: Human performance machine performance ③3 3: Machine performance >human 4: Machine performance is so poor that ⑤ ① the functions should be allocated to umans 5: Human performance is so poor that the functions should be allocated to machine 6: Unacceptable performance by both human and machine ④ Three function allocation criteria Balance of value Unsatisfactory Human Excellent Utilitarian cost-based allocation Allocation for affective or cognitive support Price. 1985

Function Allocation Criteria 16.422 1: No difference in the relative capabilities of human & machine. 2: Human performance > machine performance. 3: Machine performance > hu m an. 4: Machine performance is so poor that the functions should be allocated to humans. 5: Hu m an performance is so poor that the functions should be allocated to machine. 6: Unacceptable performance by both human and machine. Three function allocation criteria: • Balance of value • Utilitarian & cost-based allocation • Allocation for affective or cognitive support. 1 2 3 4 5 6 Unsatisfactory Human Excellent Unsatisfactory Machine Excellent Price, 1985