Skip to main content

2018.03.13 Meeting Notes


INCOSE Augmented Intelligence Challenge Team
2018.03.13 Meeting Notes
Attendance: Mark Petrotta, Troy Peterson, Bill Schindel

Agenda- Charter, Goals, Measures of Success

Goal 1: Develop a conceptual framework for Aug Int
See attached file, particularly Slide 31

Using agile SE reference architecture as example, three major system boundaries (slide 18)
               System 1: System of Model: The Target System (and Components): (Definition) The logical system of interest
               System 2: System of Engineering
               System 3: System of Innovation

(Slide 25, see red arrow)

System 2 includes Learning & Knowledge Manager for Target System (and Components): Responsible for learning new
things about the Target System, its Components, and its Environment. This may include extraction of
patterns or other knowledge from observations, planning experiments and extracting conclusions from
their results, and other forms of learning. It also includes responsibility for accumulation and
persistent memory of those learnings, and for providing the resulting knowledge for use by the LC
Managers of the Target System.

In short, move the focus of modeling from “one-off” modeling efforts (e.g. System 1), towards developing a System 2 that will “create” a System 1.

Recommend adapting this conceptual framework for Aug Int.

Goal 2: Develop history of how we collaborate / interact with models

Goal 3: Define what is “under control of model” for System 2 vs System 1
Slide 32, see “Pattern repository”
Exchange of knowledge and observations between the learning & knowledge manager (pattern repository) and target systems

Goal 4:  Define what Augmented Intelligence means in a SE process

Goal 5: How to introduce learned information to SE process
Right information at the right time to the right stakeholder, without “overwhelming” the human or computational resources

Goal 6: Develop an example (like 30m telescope)
Illustrate using a System 2 to architect a System 1.

Goal 7 : Case Studies

Goal 8 : Current State of Art
What systems / tools / methods are out there currently
“gee whiz” factor

(Thanks Bill for walking us through the agile slide deck.  I found the Agile framework very helpful in framing my thoughts!)


Comments

Popular posts from this blog

2018.03.27 Meeting Notes

INCOSE Augmented Intelligence Challenge Team 2018.03.27 Meeting Notes Attendance: [X] Mark Petrotta, [X] Troy Peterson, [X]Bill Schindel, [X]Jon Wade, []Jimmy McEver, [X]Donna Rhodes Recap of last meeting Types of learning: INLINE – Real time AFTER THE FACT – what happened? Chess AI -> learns YOU -> helps you Decision -> action -> AI analyze results Person <-> AI                Helps you learn Learning & Knowledge Manager vs LC Manager(gears) Learning & Knowledge Manager LC Manager(gears) Learn new things Not learning new things Reflecting on past history Exploit what is already known Apply what is already known Applies to ISO15288 processes Applies to ISO15288 processes AI Waves: 1)       80’s AI: Knowledge capture 2)       ML/...

2018.02.27 Meeting Notes

INCOSE Augmented Intelligence Challenge Team 2018.02.27 Meeting Notes Attendance: Mark Petrotta, Troy Peterson, Bill Schindel, Jon Wade, Jimmy McEver, Donna Rhodes Introductions (mp: I captured keywords from intros) JW: Thinking machines, “centaur systems”, instrument systems->use data, devops, anthropology, failing fast->learn fast JM: Complex Systems WG@INCOSE, agile process->feedback into next interation, operational feedback from user, optimize learning, enhance acquisition&sustainment, instrument our process, AI enabled system engineering capabilities, PAL: personal assistant for learning ??: sequence of decisions, frame questions, agility / resilience – design for, Reference: ziva bjamiled / Matt French (mp: can’t find reference) DR: anthropology/cognitive science, humans&systems, iterative-human/model interaction, BS: model based patterns, reference models, experimentation, reasoning, Meet every other week, same time