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Version 27.1 by Dongxu Lu on 2023/04/20 20:58
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1 === **Principles** ===
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3 The development of interactive, human-centered automation should be built on theory and empirical research. To support the research & development processes systematically, a Socio-Cognitive Engineering (SCE) method was constructed for building, maintaining and re-using design knowledge based on the following principles:
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5 * Creating human-centered automation is a multi-disciplinary collaborative activity
6 * Functional modules are defined and tested incrementally in an iterative refinement process
7 * Design decisions are explicitly based on claims analyses, explicating the up-downside tradeoffs
8 * Keeping and sharing the design rationale is key for progress and coherence in automation development
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11 === **Origin** ===
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13 In an international project, the European Space Agency asked to establish a sound requirements baseline for a "Mission Execution Crew Assistant" (MECA) for future manned deep space missions (e.g. to Mars). As a concise method was lacking for the research & development of the envisioned human-automation system, the first version of the SCE methodology was constructed and applied. This methodology combines approaches from user-centered design, cognitive engineering and requirements analyses to establish a coherent "self-explaining" requirements baseline consisting of:
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15 1. The **foundation** that captures the relevant domain, human factors and technological knowledge.
16 1. The **specification **of the objectives, use cases, functions (requirements) and the (expected) effects (//claims//).
17 1. The **evaluation **validates these //claims// and advances the foundation knowledge.
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19 The SCE activities that provide these outcomes can be performed in parallel. At "some time" they will be integrated into an evaluation (i.e., a prototype or simulation). For this we distinguish development **cycles**. Each development cycle provides a next version of a prototype. **Milestones** are specified for the SCE outcomes that need to be finished for such an evaluation (//note~:// a demonstration can be viewed as a very minimal evaluation).
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21 For agile R&D, SCE defines the **Minimal Viable Product (MVP)** as a coherent and concise set of (interim) SCE outcomes, i.e. a coherent set of milestones that lead to the envisioned prototype or simulation.
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23 WiSCE is the successor to the Socio-Cognitive Engineering Tool (SCET) that was hosted on Atlassian Confluence. WiSCE provides **design rationale** templates and **links** design concepts to each other (see the [[SCE Guide>>url:https://confluence.ewi.tudelft.nl/display/SG]]). The top menu of WiSCE shows the SCE components (i.e., the "boxes" of the Figure: Foundation, Specification and Evaluation), the "meta-models" (i.e., Ontology and Design Patterns, and reference items. General information about the Socio-Cognitive Engineering methodology can be found at [[http:~~/~~/scetool.ewi.tudelft.nl/;>>url:https://confluence.ewi.tudelft.nl/pages/removepage.action?pageId=59539816]] an example application is provided by Neerincx et al. [[^^~[1~] ^^>>url:https://confluence.ewi.tudelft.nl/display/SE/SCE+Example+Home#cite-summary-1-1]]([[https:~~/~~/doi.org/10.3389/frobt.2019.00118>>url:https://doi.org/10.3389/frobt.2019.00118]]).
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25 === **Method** ===
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28 [[image:/xwiki/bin/get/Main/?sheet=CKEditor.ResourceDispatcher&outputSyntax=plain&language=en&type=attach&typed=true&reference=SCE.PNG||height="396" width="750"]]**[[image:attach:SCE.PNG]]**
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31 **Figure:** Socio-Cognitive Engineering method (SCE) with three main components (Foundation, Specification and Evaluation) and the underlying or abstracted behavioral & declarative design knowledge (resp. design patterns and ontology).
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33 * The **Foundation **describes the
34 ** //Operational Demands// (e.g., stakeholders values and needs, problem scenarios, work context),
35 ** //Technology //that will be used and/or (further) developed (e.g., cloud computing, AI frameworks) and
36 ** //Human Factors// knowledge that should be addressed in the design and evaluation of the technology to meet the operational demands.
37 * The **Specification **defines the
38 ** //Objectives//: the target outcomes
39 ** //Use cases//: how the human-machine collaboration takes place, i.e., the structure and flow of actors' actions with the task allocations (who, when, where),
40 ** //Function //(requirement), i.e., what the machine shall do to serve the objectives in the corresponding use cases,
41 ** //Claim//, specifiying the expected //Effect //of the situated Function (i.e., situated in the use case) to provide the justification (why).
42 * The **Evaluation **provides the outcomes of the tests with the Prototype and/or Simulation.
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44 The SCE method is iterative in nature, which means that usually several cycles of designing and testing are required to eventually arrive at a prototype or simulation. The generated behavioral and declarative design knowledge is formalized and maintained for re-use and sharing via, respectively, **Design Patterns** and a corresponding **Ontology**.
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46 Detailed information on the methodology can be found in the [[publications section>>url:https://scetool.ewi.tudelft.nl/?q=node/5]] of this site.
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48 === References ===
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51 |1.|[[1 >>url:https://confluence.ewi.tudelft.nl/display/SE/SCE+Example+Home#cite-1-1]](((
52 Neerincx, M.A. //et al.// (2019). “Socio-Cognitive Engineering of a Robotic Partner for Child’s Diabetes Self-Management,” //Frontiers in Robotics and AI//, vol. 6, [[https:~~/~~/doi.org/10.3389/frobt.2019.00118>>url:https://doi.org/10.3389/frobt.2019.00118]].
53 )))