Category Archives: behavior function

CASE STUDIES

eJournal: uffmm.org
ISSN 2567-6458, 4.May  – 16.March   2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

In this section several case studies will  be presented. It will be shown, how the DAAI paradigm can be applied to many different contexts . Since the original version of the DAAI-Theory in Jan 18, 2020 the concept has been further developed centering around the concept of a Collective Man-Machine Intelligence [CM:MI] to address now any kinds of experts for any kind of simulation-based development, testing and gaming. Additionally the concept  now can be associated with any kind of embedded algorithmic intelligence [EAI]  (different to the mainstream concept ‘artificial intelligence’). The new concept can be used with every normal language; no need for any special programming language! Go back to the overall framework.

COLLECTION OF PAPERS

There exists only a loosely  order  between the  different papers due to the character of this elaboration process: generally this is an experimental philosophical process. HMI Analysis applied for the CM:MI paradigm.

 

JANUARY 2021 – OCTOBER 2021

  1. HMI Analysis for the CM:MI paradigm. Part 1 (Febr. 25, 2021)(Last change: March 16, 2021)
  2. HMI Analysis for the CM:MI paradigm. Part 2. Problem and Vision (Febr. 27, 2021)
  3. HMI Analysis for the CM:MI paradigm. Part 3. Actor Story and Theories (March 2, 2021)
  4. HMI Analysis for the CM:MI paradigm. Part 4. Tool Based Development with Testing and Gaming (March 3-4, 2021, 16:15h)

APRIL 2020 – JANUARY 2021

  1. From Men to Philosophy, to Empirical Sciences, to Real Systems. A Conceptual Network. (Last Change Nov 8, 2020)
  2. FROM DAAI to GCA. Turning Engineering into Generative Cultural Anthropology. This paper gives an outline how one can map the DAAI paradigm directly into the GCA paradigm (April-19,2020): case1-daai-gca-v1
  3. CASE STUDY 1. FROM DAAI to ACA. Transforming HMI into ACA (Applied Cultural Anthropology) (July 28, 2020)
  4. A first GCA open research project [GCA-OR No.1].  This paper outlines a first open research project using the GCA. This will be the framework for the first implementations (May-5, 2020): GCAOR-v0-1
  5. Engineering and Society. A Case Study for the DAAI Paradigm – Introduction. This paper illustrates important aspects of a cultural process looking to the acting actors  where  certain groups of people (experts of different kinds) can realize the generation, the exploration, and the testing of dynamical models as part of a surrounding society. Engineering is clearly  not  separated from society (April-9, 2020): case1-population-start-part0-v1
  6. Bootstrapping some Citizens. This  paper clarifies the set of general assumptions which can and which should be presupposed for every kind of a real world dynamical model (April-4, 2020): case1-population-start-v1-1
  7. Hybrid Simulation Game Environment [HSGE]. This paper outlines the simulation environment by combing a usual web-conference tool with an interactive web-page by our own  (23.May 2020): HSGE-v2 (May-5, 2020): HSGE-v0-1
  8. The Observer-World Framework. This paper describes the foundations of any kind of observer-based modeling or theory construction.(July 16, 2020)
  9. CASE STUDY – SIMULATION GAMES – PHASE 1 – Iterative Development of a Dynamic World Model (June 19.-30., 2020)
  10. KOMEGA REQUIREMENTS No.1. Basic Application Scenario (last change: August 11, 2020)
  11. KOMEGA REQUIREMENTS No.2. Actor Story Overview (last change: August 12, 2020)
  12. KOMEGA REQUIREMENTS No.3, Version 1. Basic Application Scenario – Editing S (last change: August 12, 2020)
  13. The Simulator as a Learning Artificial Actor [LAA]. Version 1 (last change: August 23, 2020)
  14. KOMEGA REQUIREMENTS No.4, Version 1 (last change: August 26, 2020)
  15. KOMEGA REQUIREMENTS No.4, Version 2. Basic Application Scenario (last change: August 28, 2020)
  16. Extended Concept for Meaning Based Inferences. Version 1 (last change: 30.April 2020)
  17. Extended Concept for Meaning Based Inferences – Part 2. Version 1 (last change: 1.September 2020)
  18. Extended Concept for Meaning Based Inferences – Part 2. Version 2 (last change: 2.September 2020)
  19. Actor Epistemology and Semiotics. Version 1 (last change: 3.September 2020)
  20. KOMEGA REQUIREMENTS No.4, Version 3. Basic Application Scenario (last change: 4.September 2020)
  21. KOMEGA REQUIREMENTS No.4, Version 4. Basic Application Scenario (last change: 10.September 2020)
  22. KOMEGA REQUIREMENTS No.4, Version 5. Basic Application Scenario (last change: 13.September 2020)
  23. KOMEGA REQUIREMENTS: From the minimal to the basic Version. An Overview (last change: Oct 18, 2020)
  24. KOMEGA REQUIREMENTS: Basic Version with optional on-demand Computations (last change: Nov 15,2020)
  25. KOMEGA REQUIREMENTS:Interactive Simulations (last change: Nov 12,2020)
  26. KOMEGA REQUIREMENTS: Multi-Group Management (last change: December 13, 2020)
  27. KOMEGA-REQUIREMENTS: Start with a Political Program. (last change: November 28, 2020)
  28. OKSIMO SW: Minimal Basic Requirements (last change: January 8, 2021)

 

 

STARTING WITH PYTHON3 – The very beginning – part 9

Journal: uffmm.org,
ISSN 2567-6458, July 24-25, 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email:gerd@doeben-henisch.de

CONTEXT

This is the next step in the python3 programming project. The overall context is still the python Co-Learning project.

SUBJECT

In this file you will see a first encounter between the AAI paradigm (described in the theory part of this uffmm blog) and some applications of the python programming language. A simple virtual world with objects and actors can become activated with a free selectable size, amount of objects and amount of actors. In later post lots of experiments with this virtual world will be described as well as many extensions.

SOURCE CODE
Main file: vw4.py

The main file ‘vw4.py’ describes the start of a virtual world and then allows a loop to run this world n-many times.

Import file: vwmanager.py

The main file ‘vw4.py’ is using many functions to enable the process. All these functions are collected in the file ‘vwmanager.py’. This file will automatically be loaded during run time of the program vw4.py.

COMMENTS

comment-vw4

DEMO

TEST RUN AUG 19, 2919, 12:56h

gerd@Doeben-Henisch:~/code$ python3 vw4.py
Amount of information: 1 is maximum, 0 is minimum0
Number of columns (= equal to rows!) of 2D-grid ?4
[‘_’, ‘_’, ‘_’, ‘_’]

[‘_’, ‘_’, ‘_’, ‘_’]

[‘_’, ‘_’, ‘_’, ‘_’]

[‘_’, ‘_’, ‘_’, ‘_’]

Percentage (as integer) of obstacles in the 2D-grid?77
Percentage (as integer) of Food Objects in the 2D-grid ?44
Percentage (as integer) of Actor Objects in the 2D-grid ?15

Objects as obstacles

[0, 2, ‘O’]

[0, 3, ‘O’]

[1, 2, ‘O’]

[2, 3, ‘O’]

Objects as food

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Objects as actor

[1, 3, ‘A’, [0, 1000, 100, 500, 0]]

[3, 2, ‘A’, [1, 1000, 100, 500, 0]]

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘A’, ‘F’]

END OF PREPARATION

WORLD CYCLE STARTS

—————————————————-
Real percentage of obstacles = 25.0
Real percentage of food = 37.5
Real percentage of actors = 12.5
—————————————————-
How many CYCLES do you want?25
Singe Step = 1 or Continous = 0?1
Length of olA 2

—————————————————–

WORLD AT CYCLE = 0

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘A’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 1000, 100, 500, -1]]

[2, 1, ‘A’, [1, 1000, 100, 500, 8]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 1

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘A’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 900, 100, 500, -1]]

[2, 1, ‘A’, [1, 900, 100, 500, 0]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 2

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘A’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 800, 100, 500, -1]]

[1, 1, ‘A’, [1, 1300, 100, 500, 1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 3

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 700, 100, 500, -1]]

[2, 0, ‘A’, [1, 1700, 100, 500, 6]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 500, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 4

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘A’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 600, 100, 500, -1]]

[1, 0, ‘A’, [1, 1600, 100, 500, 1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 700, 100]]

[2, 0, ‘F’, [2, 600, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 5

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 500, 100, 500, -1]]

[1, 1, ‘A’, [1, 2000, 100, 500, 3]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 300, 100]]

[2, 0, ‘F’, [2, 700, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 6

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 400, 100, 500, -1]]

[1, 1, ‘A’, [1, 1900, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 400, 100]]

[2, 0, ‘F’, [2, 800, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 7

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 300, 100, 500, -1]]

[1, 1, ‘A’, [1, 1800, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 900, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 8

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 200, 100, 500, -1]]

[1, 1, ‘A’, [1, 1700, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 9

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 100, 100, 500, 0]]

[1, 0, ‘A’, [1, 1600, 100, 500, 7]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 700, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 10

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 0, ‘A’, [1, 1500, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 800, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 11

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 0, ‘A’, [1, 1400, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 900, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 12

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 0, ‘A’, [1, 1300, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 13

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[2, 0, ‘A’, [1, 1700, 100, 500, 5]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 500, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 14

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘A’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 1, ‘A’, [1, 2100, 100, 500, 2]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 600, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 15

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2500, 100, 500, 8]]

[0, 0, ‘F’, [0, 500, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 700, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 16

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2400, 100, 500, -1]]

[0, 0, ‘F’, [0, 600, 100]]

[1, 1, ‘F’, [1, 700, 100]]

[2, 0, ‘F’, [2, 800, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 17

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2300, 100, 500, -1]]

[0, 0, ‘F’, [0, 700, 100]]

[1, 1, ‘F’, [1, 800, 100]]

[2, 0, ‘F’, [2, 900, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 18

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2200, 100, 500, -1]]

[0, 0, ‘F’, [0, 800, 100]]

[1, 1, ‘F’, [1, 900, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 19

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2100, 100, 500, -1]]

[0, 0, ‘F’, [0, 900, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 20

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2000, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 21

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 1900, 100, 500, 0]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 22

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 1, ‘A’, [1, 1800, 100, 500, 3]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 23

[‘F’, ‘A’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 1, ‘A’, [1, 2200, 100, 500, 5]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 24

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 1, ‘A’, [1, 2100, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

 

ACTOR-ACTOR INTERACTION [AAI] WITHIN A SYSTEMS ENGINEERING PROCESS (SEP). An Actor Centered Approach to Problem Solving

eJournal: uffmm.org, ISSN 2567-6458
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

ATTENTION: The actual Version  you will find HERE.

Draft version 22.June 2018

Update 26.June 2018 (Chapter AS-AM Summary)

Update 4.July 2018 (Chapter 4 Actor Model; improving the terminology of environments with actors, actors as input-output systems, basic and real interface, a first typology of input-output systems…)

Update 17.July 2018 (Preface, Introduction new)

Update 19.July 2018 (Introduction final paragraph!, new chapters!)

Update 20.July 2018 (Disentanglement of chapter ‘Simulation & Verification’ into two independent chapters; corrections in the chapter ‘Introduction’; corrections in chapter ‘AAI Analysis’; extracting ‘Simulation’ from chapter ‘Actor Story’ to new chapter ‘Simulation’; New chapter ‘Simulation’; Rewriting of chapter ‘Looking Forward’)

Update 22.July 2018 (Rewriting the beginning of the chapter ‘Actor Story (AS)’, not completed; converting chapter ‘AS+AM Summary’ to ‘AS and AM Philosophy’, not completed)

Update 23.July 2018 (Attaching a new chapter with a Case Study illustrating an actor story (AS). This case study is still unfinished. It is a case study of  a real project!)

Update 7.August 2018 (Modifying chapter Actor Story, the introduction)

Update 8.August 2018 (Modifying chapter  AS as Text, Comic, Graph; especially section about the textual mode and the pictorial mode; first sketch for a mapping from the textual mode into the pictorial mode)

Update 9.August 2018 (Modification of the section ‘Mathematical Actor Story (MAS) in chapter 4).

Update 11.August 2018 (Improving chapter 3 ‘Actor Story; nearly complete rewriting of chapter 4 ‘AS as text, comic, graph’.)

Update 12.August 2018 (Minor corrections in the chapters 3+4)

Update 13.August 2018 (I am still catched by the chapters 3+4. In chapter  the cognitive structure of the actors has been further enhanced; in chapter 4 a complete example of a mathematical actor story could now been attached.)

Update 14.August 2018 (minor corrections to chapter 4 + 5; change-statements define for each state individual combinatorial spaces (a little bit like a quantum state); whether and how these spaces will be concretized/ realized depends completely from the participating actors)

Update 15.August 2018 (Canceled the appendix with the case study stub and replaced it with an overview for  a supporting software tool which is needed for the real usage of this theory. At the moment it is open who will write the software.)

Update 2.October 2018 (Configuring the whole book now with 3 parts: I. Theory, II. Application, III. Software. Gerd has his focus on part I, Zeynep will focus on part II and ‘somebody’ will focus on part III (in the worst case we will — nevertheless — have a minimal version :-)). For a first quick overview about everything read the ‘Preface’ and the ‘Introduction’.

Update 4.November 2018 (Rewriting the Introduction (and some minor corrections in the Preface). The idea of the rewriting was to address all the topics which will be discussed in the book and pointing out to the logical connections between them. This induces some wrong links in the following chapters, which are not yet updated. Some chapters are yet completely missing. But to improve the clearness of the focus and the logical inter-dependencies helps to elaborate the missing texts a lot. Another change is the wording of the title. Until now it is difficult to find a title which is exactly matching the content. The new proposal shows the focus ‘AAI’ but lists the keywords of the main topics within AAA analysis because these topics are usually not necessarily associated with AAI.)

ACTOR-ACTOR INTERACTION [AAI]. An Actor Centered Approach to Problem Solving. Combining Engineering and Philosophy

by

GERD DOEBEN-HENISCH in cooperation with  LOUWRENCE ERASMUS, ZEYNEP TUNCER

LATEST  VERSION AS PDF

BACKGROUND INFORMATION 19.Dec.2018: Application domain ‘Communal Planning and e-Gaming’

BACKGROUND INFORMATION 24.Dec.2018: The AAI-paradigm and Quantum Logic

PRE-VIEW: NEW EXPANDED AAI THEORY 23.January 2019: Outline of the new expanded  AAI Paradigm. Before re-writing the main text with these ideas the new advanced AAI theory will first be tested during the summer 2019 within a lecture with student teams as well as in  several workshops outside the Frankfurt University of Applied Sciences with members of different institutions.

AASE – Actor-Actor Systems Engineering. Theory & Applications. Micro-Edition (Vers.9)

eJournal: uffmm.org, ISSN 2567-6458
13.June  2018
Email: info@uffmm.org
Authors: Gerd Doeben-Henisch, Zeynep Tuncer,  Louwrence Erasmus
Email: doeben@fb2.fra-uas.de
Email: gerd@doeben-henisch.de

PDF

CONTENTS

1 History: From HCI to AAI …
2 Different Views …
3 Philosophy of the AAI-Expert …
4 Problem (Document) …
5 Check for Analysis …
6 AAI-Analysis …
6.1 Actor Story (AS) . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Textual Actor Story (TAS) . . . . . . . . . . . . . . .
6.1.2 Pictorial Actor Story (PAT) . . . . . . . . . . . . . .
6.1.3 Mathematical Actor Story (MAS) . . . . . . . . . . .
6.1.4 Simulated Actor Story (SAS) . . . . . . . . . . . . .
6.1.5 Task Induced Actor Requirements (TAR) . . . . . . .
6.1.6 Actor Induced Actor Requirements (UAR) . . . . . .
6.1.7 Interface-Requirements and Interface-Design . . . .
6.2 Actor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.1 Actor and Actor Story . . . . . . . . . . . . . . . . .
6.2.2 Actor Model . . . . . . . . . . . . . . . . . . . . . .
6.2.3 Actor as Input-Output System . . . . . . . . . . . .
6.2.4 Learning Input-Output Systems . . . . . . . . . . . .
6.2.5 General AM . . . . . . . . . . . . . . . . . . . . . .
6.2.6 Sound Functions . . . . . . . . . . . . . . . . . . .
6.2.7 Special AM . . . . . . . . . . . . . . . . . . . . . .
6.2.8 Hypothetical Model of a User – The GOMS Paradigm
6.2.9 Example: An Electronically Locked Door . . . . . . .
6.2.10 A GOMS Model Example . . . . . . . . . . . . . . .
6.2.11 Further Extensions . . . . . . . . . . . . . . . . . .
6.2.12 Design Principles; Interface Design . . . . . . . . .
6.3 Simulation of Actor Models (AMs) within an Actor Story (AS) .
6.4 Assistive Actor-Demonstrator . . . . . . . . . . . . . . . . . .
6.5 Approaching an Optimum Result . . . . .
7 What Comes Next: The Real System
7.1 Logical Design, Implementation, Validation . . . .
7.2 Conceptual Gap In Systems Engineering? . . .
8 The AASE-Paradigm …
References

Abstract

This text is based on the the paper “AAI – Actor-Actor Interaction. A Philosophy of Science View” from 3.Oct.2017 and version 11 of the paper “AAI – Actor-Actor Interaction. An Example Template” and it   transforms these views in the new paradigm ‘Actor- Actor Systems Engineering’ understood as a theory as well as a paradigm for and infinite set of applications. In analogy to the slogan ’Object-Oriented Software Engineering (OO SWE)’ one can understand the new acronym AASE as a systems engineering approach where the actor-actor interactions are the base concepts for the whole engineering process. Furthermore it is a clear intention to view the topic AASE explicitly from the point of view of a theory (as understood in Philosophy of Science) as well as from the point of view of possible applications (as understood in systems engineering). Thus the classical term of Human-Machine Interaction (HMI) or even the older Human-Computer Interaction (HCI) is now embedded within the new AASE approach. The same holds for the fuzzy discipline of Artificial Intelligence (AI) or the subset of AI called Machine Learning (ML). Although the AASE-approach is completely in its beginning one can already see how powerful this new conceptual framework  is.

 

 

ACTOR-ACTOR INTERACTION. Philosophy of the Actor

eJournal: uffmm.org, ISSN 2567-6458
16.March 2018
Email: info@uffmm.org
Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Frankfurt University of Applied Sciences (FRA-UAS)
Institut for New Media (INM, Frankfurt)

PDF

CONTENTS

I   A Vision as a Problem to be Solved … 1
II   Language, Meaning & Ontology …  2
     II-A   Language Levels . . . . . . . . .  . . 2
     II-B  Common Empirical Matter .  . . . . . 2
     II-C   Perceptual Levels . . . . . . .  . . . . 3
     II-D   Space & Time . . . . . . . .  . . . . . 4
     II-E    Different Language Modes . . . 4
     II-F    Meaning of Expressions & Ontology … 4
     II-G   True Expressions . . . . . . .  . . . .  5
     II-H   The Congruence of Meaning  . . . .  5
III   Actor Algebra … 6
IV   World Algebra  … 7
V    How to continue … 8
VI References … 8

Abstract

As preparation for this text one should read the chapter about the basic layout of an Actor-Actor Analysis (AAA) as part of an systems engineering process (SEP). In this text it will be described which internal conditions one has to assume for an actor who uses a language to talk about his observations oft he world to someone else in a verifiable way. Topics which are explained in this text are e.g. ’language’,’meaning’, ’ontology’, ’consciousness’, ’true utterance’, ’synonymous expression.

AAI – Actor-Actor Interaction. A Toy-Example, No.1

eJournal: uffmm.org, ISSN 2567-6458
13.Dec.2017
Email: info@uffmm.org

Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Contents

1 Problem ….. 3
2 AAI-Check ….. 3
3 Actor-Story (AS) …..  3
3.1 AS as a Text . . . . . . . . . . . . . . . . . .3
3.2 Translation of a Textual AS into a Formal AS …… 4
3.3 AS as a Formal Expression . . . . . . . . . .4
3.4 Translation of a Formal AS into a Pictorial AS… 5
4 Actor-Model (AM) …..  5
4.1 AM for the User as a Text . . . . . . . . . . . . . . . . . . . . . . . . . .  . . . .6
4.2 AM for the System as a Text . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
5 Combined AS and AM as a Text …..  6
5.1 AM as an Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
6 Simulation …..  7
6.1 Simulating the AS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
6.2 Simulating the AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
6.3 Simulating AS with AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
7 Appendix: Formalisms ….. 8
7.1 Set of Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
7.2 Predicate Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
8 Appendix: The Meaning of Expressions …11
8.1 States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2 Changes by Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Abstract

Following the general concepts of the paper ’AAI – Actor-Actor Interaction. A Philosophy of Science View’ from 3.Oct.2017 this paper illustrates a simple application where the difference as well as the
interaction between an actor story and several actor models is shown. The details of interface-design as well as the usability-testing are not part of this example.(This example replaces the paper with the title
’AAI – Case Study Actor Story with Actor Model. Simple Grid-Environment’ from 15.Nov.2017). One special point is the meaning of the formal expressions of the actor story.

Attention: This toy example is not yet in fully conformance with the newly published Case-Study-Template

To read the full text see PDF

Clearly, one can debate whether a ‘toy-example’ makes sens, but the complexity of the concepts in this AAI-approach is to great to illustrate these in the beginning  with a realistic example without loosing the idea. The author of the paper has tried many — also very advanced — versions in the last years and this is the first time that he himself has the feeling that at least the idea is now clear enough. And from teaching students it is very clear, if you cannot explain an idea in a toy-example you never will be able to apply it to real big problems…

 

INTELLIGENT MACHINES – INTRODUCTION

eJournal: uffmm.org,
ISSN 2567-6458, 09.Oct 2017 – April 9, 2022, 13:30 h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

 Remark April 2022

This post from Oct 2017 will be reviewed in the new conceptual framework of an Applied Empirical Theory [AET] with an additional Dynamic Format [DF]. For more details see HERE.

OVERVIEW

A short story telling You, (i) how we interface the intelligent machines (IM) part with the actor-actor interaction (AAI) part, (ii) a first working definition of intelligent machines (IM) in this text, and (iii) defining intelligence and how one can this measure.

IM WITHIN AAI

In this blog we see IM not isolated, as a stand alone endeavor, but as embedded in a discipline called actor-actor interaction (AAI)(later called DAAI := Distributed Actor Actor Interaction).  AAI investigates complex tasks and looks how different kinds of actors are interacting in these contexts with technical systems. As far as the participating systems have been technical systems one speaks here of a system interface (SI) as that part of a technical system, which is interacting with the human actor. In the case of biological systems (mostly humans, but it could be animals as well), one speaks of the user interface (UI). In this text we generalize both cases by the general concept of an actor — biological and non-biological –, which has some actor interface (ActI), and this actor interface embraces all properties which are relevant for the interactions of the actor.

For the analysis of the behavior of actors in such task-environments one can distinguish two important concepts: the actor story (AS) describing the context as an observable process, as well as different actor models (AM). Actor models are special extensions of an actor story because an actor model describes the observable behavior of actors as a behavior function (BF) with a set of assumptions about possible internal states of the actors. The assumptions about possible internal states (IS) are either completely arbitrary or empirically motivated.

The embedding of IM within AAI can be realized through the concept of an actor model (UM) and the actor story (AS). Whatever is important for something which is called an intelligent machine application (IMA) can be defined as an actor model within an actor story. This embedding of IM within AAI offers many advantages.

This has to be explained with some more details.

An Intelligent Machine (IM) in an Actor Story

Let us assume that there exists a mathematical-graph representation of an actor story written as AS_{L_{ε}}. Such a graph has nodes which represent situations. Formally these are sets of properties, probably more fine-grained by subsets which represent different kinds of actors embedded in this situation as well as different kinds of non-actors.

Actors can be classified (as introduced above) as either biological actors (BA) or non-biological actors (NBA). Both kinds of actors can — in another reading — be subsumed under the general term of input-output-systems (IO-SYS). An input-output system can be a learning system or non-learning. Another basic property is that of being intelligent or non-intelligent. Being a learning system and being an intelligent system is usually strongly connected, but this must not necessarily be so. Being a learning system can be associated with being non-intelligent and being intelligent can be connected with being non-learning.(cf. Figure 1)

Classification of input-output systems according to learning, intelligence and beeing biological or not biological
Classification of input-output systems according to learning, intelligence and being biological or non-biological

While biological systems are always learning and intelligent, one can find non-biological systems of all types: non-learning and non-intelligent, non-intelligent and learning, non-learning and intelligent, and learning and intelligent.

Learning System

To classify a system as a learning system this requires the general ability to change the behavior of this system in time thus that there exists a time-span (t1,t2) after which the behavior as response to  certain critical stimuli has changed compared to the time before. [1] From this requirement it follows, that a learning system is an input-output system with at least one internal state which can change. Thus we have the general assumption:

Def: Learning System (LS)

  1. LS(x) iff
  2. x=<I, O, IS, phi >
  3. φ : I x IS —> IS x O
  4. I := Input
  5. O := Output
  6. IS := Internal states

Some x is a learning system (LS) if it is a structure containing sets for input (I), Output (O), as well as internal states (IS). These sets are operated by a behavior function φ which maps inputs and actual internal states to output as well as back to internal states. The set of possible learning functions is infinite.

Intelligent System

The term ‘intelligent’ and ‘intelligence’ is until now not standardized. This means that everybody is using it at little bit arbitrarily.

In this text we take the basic idea of a scientific usage of the term ‘intelligence’ from experimental psychology, which has developed clearly defined operational concepts since the end of the 19. Century which have been proved as quite stable in their empirical applications. [2a,b,c] 

The central idea of the psychological concept of the usage of the term ‘intelligence’ is to associate the usage of the term ‘intelligence’ with observable behavior of those actors, which shall be classified according defined methods of measurement.

In the case of experimental psychology the actors have been biological systems, mainly humans, in the first years of the research school children of certain ages. Because nobody did know what ‘intelligence’ means ‘as such’ one agreed to accept the observable behavior of children in certain task environments as ‘manifestations’ of a ‘presupposed unknown intelligence’. Thus the ability of children to solve defined tasks in a certain defined manner became a norm for what is called ‘intelligence’. Solving the tasks in a certain time with less than a certain amount of errors was used as a ‘baseline’ and all behavior deviating from the baseline was ‘better’ or ‘poorer’.

Thus the ‘content’ of the ‘meaning’ of the term ‘intelligence’ has been delegated to historical patterns of behavior which were common in a certain time-span in a certain geographical and cultural region.

While these behavior patterns can change during the course of time the general method of measurement is invariant.

In the time since then experimental psychology has modified and elaborated this first concept in some directions.

One direction is the modification of the kind of tasks which are used for the tests. With regard to the cultural context one has modified the content, thereby looking to find such kinds of task which seem to be ‘invariant’ with regard to the presupposed intelligence factor. This is an ongoing process.

The other direction is the focus on the actors as such. Because biological systems like humans change the development of their intelligence with age one has tried to find out ‘typical tasks for every age’. This too is an ongoing process.

This history of experimental psychology gives very interesting examples how one can approach the problem of the usage and the measurement of some X which we call ‘intelligence’.

In the context of an AAI-approach we have not only biological systems, but also non-biological systems. Thus most of the elaborated parameters of psychology for human actors are not general enough.

One possible strategy to generalize the intelligence-paradigm of experimental psychology could be to ‘free’ the selection of task sets from the narrow human cultures of the past and require only ‘clearly defined task sets with defined interfaces and defined contexts’. All these tasks sets can be arranged either in one super-set or in a parameterized field of sets. The sum of all these sets defines then a space of possible behavior and associated with this a space of possible measurable intelligence.

A task has then to be given as an actor story according to the AAI-paradigm. Such a specified actor story allows the formal definition of a complexity measure which can be used to measure the ‘amount of intelligence necessary to solve such a task’.

With such a more general and extendable approach to the measurement of observable intelligence one can compare all kinds of systems with each other. With such an approach one can further show objectively, where biological and non-biological systems differ generally, where they are similar, and to which extend they differ with regard to concrete circumstances.

Measuring Intelligence by Actor Stories

Presupposing actor stories (AS) (ideally formalized as mathematical graphs) on can define a first operational general measurement of intelligence.

Def: Task-Intelligence of a task τ (TInt(τ))

    1. Every defined task τ represents a graph g with one shortest path pmin(τ)= π_{min} from a start node to a goal node.
    2. Every such shortest path π_{min} has a certain number of nodes path-nodes(π_{min})=ν.
    3. The number of solved nodes (ν_{solved}) can become related against the total number of nodes ν as ν_{solved}/ν. We take TInt(τ)= ν_{solved}/ν. It follows that TInt(τ) is between 0 and 1: 0 ≤ TInt(τ)≤ 1.
    4. To every task  a maximal duration Δ_{max} is attached; all nodes which are solved within this maximal duration time Δ_{max} are declared as ‘solved’, all the others as ‘un-solved’.

The usual case will require more than one task to be realized. Thus we introduce the concept of a task field (TF).

Def: Task-Field of type x (TF_{x})
Def: Task-Field Intelligence (TFInt)

A task-field TF of type x includes a finite set of individual tasks like TF_{x} = { τ{x.1}, τ{x.2}, … , τ{x.n} } with n ≥ 2. The sum of all individual task intelligence values TInt(τ{x.i}) has to be normalized to 1, i.e. (TInt(τ{x.1}) + TInt(τ{x.2}) + … + TInt(τ{x.n}))/ n (with 0 in the nominator not allowed). Thus the value of the intelligence of a task field of type x TFInt(TF_{x}) is again in the domain of [0,1].

Because the different tasks in a task field TF can be of different difficulty it should be possible to introduce some weighting for the individual task intelligence values. This should not change the general mechanism.

Def: Combined Task-Fields (TF)

In face of the huge variety of possible task fields in this world it can make sens to introduce more general layers by grouping task fields of different types together to larger combined fields, like TF_{x,…,z} = TF_{x} ∪ TF_{y} ∪ … ∪ TF_{z}. The task field intelligence TFInt of such combined task fields would be computed as before.

Def: Omega Task-Field at time t (TF_{ω}(t))

The most comprehensive assembly of such combinations shall here be called the Omega-Task-Field at time t TF_{ω}(t). This indicates the known maximum of intelligence measurements at that point of time.

Measurement Comments

With these assumptions the term intelligence will be restricted to clearly defined domains either to an individual task, to a task-field of type x, or to some grouped task-fields or being related to the actual omega task-field. In every such domain the intelligence value is in the realm of [0,1] or written as some value between 0 or 100%.

Independent of the type of an actor — biological or not — one can measure the intelligence of such an actor with the same domains of defined tasks. As a result one can easily compare all known actors with regard to such defined task domains.

Because the acting actors can be quite different by their input-output capabilities it follows that every actor has to organize some interface which enables him to use the defined task. There are no special restrictions to the format of such an interface, but there is one requirement which has to be observed strictly: the interface as such is not allowed to do any kind of computation beyond providing only the necessary input from the task domain or to provide the necessary output to the domain. Only then are the different tests able to reveal some difference between the different actors.

If the tests show differences between certain types of actors with regard to a certain task or a task-field then this is a chance to develop smart assistive interfaces which can help the actor in question to overcome his weakness compared to the other type of actor. Thus this kind of measuring intelligence can be a strong supporter for a better world in the future.

Another consequence of the differing intelligence values can be to look to the inner structure of an actor with weaker values and asking how one could improve his capabilities. This can be done e.g. by different kinds of training or by improving his system structures.

COMMENTS

[1] Sara J.Shettleworth, Biological Approaches to the Study of Learning, pp.185 – 219, in: N.J.Mackintosh (Ed.), Animal Learning and Cognition, Academic Press, San Diego, New York, London et.al., 1994

[2a] Ernest R.Hilgard, Rita L.Atkinson, Richard C.Atkinson, Introduction to Psychology, Harcourt Brace Jovanovic, Inc., Psychology, 7th ed., New York, San Diego, Chicago et.al, 1979

[2b] Detlef H.Rost, Intelligenz. Fakten und Mythen, Belz Verlag, Weinheim – Basel, 2009

[2c] Detlef H.Rost, Handbuch Intelligenz, Beltz Verlag, Weinheim – Basel, 2013

uffmm – RESTART AS SCIENTIFIC WORKPLACE

RESTART OF UFFMM AS SCIENTIFIC WORKPLACE.
For the Integrated Engineering of the Future (SW4IEF)
Campaining the Actor-Actor Systems Engineering (AASE) paradigm

eJournal: uffmm.org, ISSN 2567-6458
Email: info@uffmm.org

Last Update June-22, 2018, 15:32 CET.  See below: Case Studies —  Templates – AASE Micro Edition – and Scheduling 2018 —

RESTART

This is a complete new restart of the old uffmm-site. It is intended as a working place for those people who are interested in an integrated engineering of the future.

SYSTEMS ENGINEERING

A widely known and useful concept for a general approach to the engineering of problems is systems engineering (SE).

Open for nearly every kind of a possible problem does a systems engineering process (SEP) organize the process how to analyze the problem, and turn this analysis into a possible design for a solution. This proposed solution will be examined by important criteria and, if it reaches an optimal version, it will be implemented as a real working system. After final evaluations this solution will start its carrier in the real world.

PHILOSOPHY OF SCIENCE

In a meta-scientific point of view the systems engineering process can become itself the object of an analysis. This is usually done by a discipline called philosophy of science (PoS). Philosophy of science is asking, e.g., what the ‘ingredients’ of an systems-engineering process are, or how these ingredients do interact? How can such a process ‘fail’? ‘How can such a process be optimized’? Therefore a philosophy of science perspective can help to make a systems engineering process more transparent and thereby supports an optimization of these processes.

AAI (KNOWN AS HMI, HCI …)

A core idea of the philosophy of science perspective followed in this text is the assumption, that a systems engineering process is primarily based on different kinds of actors (AC) whose interactions enable and direct the whole process. These assumptions are also valid in that case, where the actors are not any more only biological systems like human persons and non-biological systems called machines, but also in that case where the traditional machines (M) are increasingly replaced by ‘intelligent machines (IM)‘. Therefore the well know paradigm of human-machine interaction (HMI) — or earlier ‘human-computer interaction (HCI)’  will be replaced in this text by the new paradigm of Actor-Actor Interaction (AAI). In this new version the main perspective is not the difference of man on one side and machines on the other but the kind of interactions between actors of all kind which are necessary and possible.

INTELLIGENT MACHINES

The  concept of intelligent machines (IM) is understood here as a special case of the general Actor (A) concept which includes as other sub-cases biological systems, predominantly humans as instantiations of the species Homo Sapiens. While until today the question of biological intelligence and machine intelligence is usually treated separately and differently it is intended in this text to use one general concept of intelligence for all actors. This allows then more direct comparisons and evaluations. Whether biological actors are in some sense better than the non-biological actors or vice versa can seriously only be discussed when the used concept of intelligence is the same.

ACTOR STORY AND ACTOR MODELS

And, as it will be explained in the following sections, the used paradigm of actor-actor interactions uses the two main concepts of actor story (AS) as well as actor model (AM). Actor models are embedded in the actor stories. Whether an actor model describes biological or non-biological actors does not matter. Independent of the inner structures of an actor model (which can be completely different) the actor story is always  completely described in terms of observable behavior which are the same for all kinds of actors (Comment: The major scientific disciplines for the analysis of behavior are biology, psychology, and sociology).

AASE PARADIGM

In analogy to the so-called ‘Object-Oriented (OO) approach in Software-Engineering (SWE)’ we campaign here the ‘Actor-Actor (AA) Systems Engineering (SE)’ approach. This takes the systems Engineering approach as a base concepts and re-works the whole framework from the point of view of the actor-actor paradigm.  AASE is seen here as a theory as well as an   domain of applications.

Ontologies of the AASE paradigm
Figure: Ontologies of the AASE paradigm

To understand the different perspectives of the used theory it can help to the figure ‘AASE-Paradigm Ontologies’. Within the systems engineering process (SEP) we have AAI-experts as acting actors. To describe these we need a ‘meta-level’ realized by a ‘philosophy of the actor’. The AAI-experts themselves are elaborating within an AAI-analysis an actor story (AS) as framework for different kinds of intended actors. To describe the inner structures of these intended actors one needs different kinds of ‘actor models’. The domain of actor-model structures overlaps with the domain of ‘machine learning (ML)’ and with ‘artificial intelligence (AI)’.

SOFTWARE

What will be described and developed separated from these theoretical considerations is an appropriate software environment which allows the construction of solutions within the AASE approach including e.g. the construction of intelligent machines too. This software environment is called in this text emerging-mind lab (EML) and it will be another public blog as well.

 

THEORY MICRO EDITION & CASE STUDIES

How we proceed

Because the overall framework of the intended integrated theory is too large to write it down in one condensed text with  all the necessary illustrating examples we decided in Dec 2017 to follow a bottom-up approach by writing primarily case studies from different fields. While doing this we can introduce stepwise the general theory by developing a Micro Edition of the Theory in parallel to the case studies. Because the Theory Micro Edition has gained a sufficient minimal completeness already in April 2018 we do not need anymore a separate   template for case studies. We will use the Theory Micro Edition  as  ‘template’ instead.

To keep the case studies readable as far as possible all needed mathematical concepts and formulas will be explained in a separate appendix section which is central for all case studies. This allows an evolutionary increase in the formal apparatus used for the integrated theory.

THEORY IN A BOOK FORMAT

(Still not final)

Here you can find the actual version of the   theory which will continuously be updated and extended by related topics.

At the end of the text you find a list of ToDos where everybody is invited to collaborate. The main editor is Gerd Doeben-Henisch deciding whether the proposal fits into the final text or not.

Last Update 22.June 2018

Philosophy of the Actor

This sections describes basic assumptions about the cognitive structure of the human AAI expert.

From HCI to AAI. Some Bits of History

This sections describes main developments in the history from HCI to AAI.

SCHEDULE 2018

The Milestone for a first outline in a book format has been reached June-22, 2018. The   milestone for a first final version   is  scheduled   for October-4, 2018.