eJournal: uffmm.org, ISSN 2567-6458,
Begin: Jan 8, 2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This python webserver programming  section is part of the uffmm science blog.

OBJECTIVE

Since Dec-23, 2020 our web-server is online under [www.]oksimo.com. The login will be reserved to selected test-groups for some time. Since Jan 6, 2021 several student teams and two other groups are tsting the software. We have selected the acronym oksimo because there existed a software project — invented and directed — by Gerd Doeben-Henisch, which has at that time gained some public awareness. But as the public awareness had reached several Million hits per day the project had to be stopped by a severe lack of resources  at that time.  The spirit of that project continued in the head of the author. And — without having a plan to revitalize a new software project — the theory (as described in this block) developed steadily and the idea of an accompanying assisting software gained momentum in the last years. Finally Tobias Schmitt and Gerd Doeben-Henisch began to write code and now something new is popping up. The code and the architecture is completely different to the old oksimo software, but the spirit is the same; indeed it is even more general and more radical. Therefore we call this ‘new oksimo’ OKSIMO(-RELOADED).

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

See part 7.

SZENARIO

Motivation

See part 7.

On this part 8 I focus on the dynamic part of the virtual world. Which kind of changes are possible and how they can be managed?

1. 
   Virtual World (VW) – General Assumptions
   See part 7.
   
   ACTOR STORY
   
   1. After completing a 2D virtual world with obstacles ‘O’, food objects ‘F’ and actors ‘A’, the general world cycling loop will be started. In this loop all possible changes will be managed. The original user has no active role during this cycling loop; he is now in the role of the administrator starting or stopping the cycling loop or   interrupt to get some data.
   2. The cycling loop can be viewed as structured in different phases.
   a) In the first phase all general services will be managed, this is the management of the energy level of all objects: (i) food objects will be updated to grow and (ii) actor objects will be updated by reducing the energy by the standard  depending from time. If energy level is below 0 then the avatar of the actor will be removed from the object list as well as from the grid.
   b) In the second phase all offered actions will be collected. The only sources for actions are so far the actors. There can either be an  (i) eat-action, if they dwell actually on a position with food, or (ii) they can make a move.
   (i) In case of an eat-action  the actor sends a message to the action buffer of the world: [ID of actor, position, eat-action]
   (ii) In case of a move-action the actor sends a message to the action buffer of the world: [ID of actor, position, move-action, direction DIR in [0,7]].
   3. The world function wf will randomly select each action message one after the other and process these. There are the following cases:
   (a) In case of an eat-action it will be checked whether there is food at the position. If not nothing will happen, if yes then the energy level of the eating actor will be increased  inside the actor and the amount of the food will be decreased. There is a correspondence between amount of food and consumed energy.
   (b) In case of a move-action it will be checked whether there is a path in the selected direction to move one cell. If not, then nothing will happen, if Yes, then the move will happen and the energy level will be decreased by a certain amount due to moving.
   3. After finishing all services the world clock WCLCK will be increased by one (Remark: in later versions it can happen that there exist actions whose duration needs more than 1 cycle to become finished).

IMPLEMENTATION

vw3.py (Main File)

gridHelper.py (Import module)

EXERCISES

One new aspect is the update of the food objects which ‘refresh’ their amount and with this amount their energy depending from the time. Here it is a simple time depending increment. In later versions one can differentiate between different foods, differen surounding conditions, etc.

def foodUpdate(olF,objL):

for i in range(len(olF)):        #Cycle through all food objects
if olF[i][3][1]<objL[‘F’][1]: #Compare with standard maximum
olF[i][3][1]+objL[‘F’][2]     # If lower then increment by standard
return olF

The other task during the object update is the management of the actor objects with regard to their energy.  They are losing energy directly depending from the time and in case of movement. During the simulation actors can increase their energy again by eating, but as long this does not happen, their energy loss is continuously in every new cycle. This can lead to the extreme situation, where an actor has reached an energy zero state (<1).  In this case the actor will be removed from the grid (replacing its old position ‘A by an empty space ‘_’). Simultaneously this actor shall be removed from the list of all actors olA.

def actorUpdate(olA,objL,mx):
   for i in range(len(olA)):                             #Cycle through all actor objects
   olA[i][3][1]=olA[i][3][1]-objL[‘A’][2] #Decrement the energy level by the   standard

# Check whether an element is below 1 with its energy
iL=[y for y in range(len(olA)) if olA[y][3][1]<1] # Generate a list of all these actors with no energy

if iL != []: # If the list is not empty:

#Collect coordinates from actors in Grid
yxL=[[olA[y][x]] for y in range(len(olA)) for x in range(2) if olA[y][3][1]<1]

# Concentrate lists as (y,x) pairs
yxc=[[yxL[i][0], yxL[i+1][0]] for i in range(0,len(yxL),2)]

# Replace selected actors in the Grid by ‘_’
if yxc != []:

for i in range(len(yxc)):
y=yxc[i][0]
x=yxc[i][1]
mx[y][x]=’_’

# Delete actor from olA list
#Because pop() decreases the olA list, one has to start indexing from the ‘right end’ because then the decrement of the list
# keeps the other remaining indices valid!

for i in range(len(iL)-1,-1,-1):  #A reverse order!
   olA.pop(iL[i])
else:
  return olA

DEMO

This demo shows that part of a run where the energy level of the actors — in this case all together, which is usually not the case — are changing from 200 to 0, which means they have reached the critical state.

olA in Update :
[[1, 1, ‘A’, [0, 200, 200, 100]], [1, 4, ‘A’, [1, 200, 200, 100]], [2, 4, ‘A’, [2, 200, 200, 100]], [4, 3, ‘A’, [3, 200, 200, 100]]]
iL before if
[]
iL is empty

In this situation is the list of actors which have reached zero energy (iL) still empty.

Updated actor objects:

[1, 1, ‘A’, [0, 200, 200, 100]]

[1, 4, ‘A’, [1, 200, 200, 100]]

[2, 4, ‘A’, [2, 200, 200, 100]]

[4, 3, ‘A’, [3, 200, 200, 100]]

[‘_’, ‘_’, ‘_’, ‘_’, ‘F’, ‘O’, ‘F’]
[‘O’, ‘A’, ‘_’, ‘F’, ‘A’, ‘O’, ‘F’]
[‘F’, ‘O’, ‘O’, ‘F’, ‘A’, ‘_’, ‘O’]
[‘_’, ‘F’, ‘_’, ‘F’, ‘O’, ‘F’, ‘F’]
[‘O’, ‘_’, ‘O’, ‘A’, ‘O’, ‘O’, ‘_’]
[‘F’, ‘_’, ‘F’, ‘_’, ‘O’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘_’, ‘O’, ‘_’]

Updated food objects:

[0, 4, ‘F’, [0, 1000, 20]]

[0, 6, ‘F’, [1, 1000, 20]]

[1, 3, ‘F’, [2, 1000, 20]]

[1, 6, ‘F’, [3, 1000, 20]]

[2, 0, ‘F’, [4, 1000, 20]]

[2, 3, ‘F’, [5, 1000, 20]]

[3, 1, ‘F’, [6, 1000, 20]]

[3, 3, ‘F’, [7, 1000, 20]]

[3, 5, ‘F’, [8, 1000, 20]]

[3, 6, ‘F’, [9, 1000, 20]]

[5, 0, ‘F’, [10, 1000, 20]]

[5, 2, ‘F’, [11, 1000, 20]]

olA in Update :
[[1, 1, ‘A’, [0, 0, 200, 100]], [1, 4, ‘A’, [1, 0, 200, 100]], [2, 4, ‘A’, [2, 0, 200, 100]], [4, 3, ‘A’, [3, 0, 200, 100]]]

Now the energy levels reached 0.

iL before if
[0, 1, 2, 3] 

# The list iL is now filled up with indices of olA which actors reached energy 0

IL inside if: [0, 1, 2, 3]
yxL inside if: [[1], [1], [1], [4], [2], [4], [4], [3]]
xyc inside :
[[1, 1], [1, 4], [2, 4], [4, 3]]

This last version of the yxc list shows the (y,x) coordinates of those actors which have energy below 1.

iL before pop :
[0, 1, 2, 3]

Then starts the pop() operation which takes those identified actors from the olA list. The removing of actors starts from the right side of the list because otherwise somewhere in the middle of the list the indices would no longer  exist any more which have been valid in the full list.

pop : 3
pop : 2
pop : 1
pop : 0

Updated actor objects:

As one can see, the actors have been removed from grid.

[‘_’, ‘_’, ‘_’, ‘_’, ‘F’, ‘O’, ‘F’]
[‘O’, ‘_’, ‘_’, ‘F’, ‘_’, ‘O’, ‘F’]
[‘F’, ‘O’, ‘O’, ‘F’, ‘_’, ‘_’, ‘O’]
[‘_’, ‘F’, ‘_’, ‘F’, ‘O’, ‘F’, ‘F’]
[‘O’, ‘_’, ‘O’, ‘_’, ‘O’, ‘O’, ‘_’]
[‘F’, ‘_’, ‘F’, ‘_’, ‘O’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘_’, ‘O’, ‘_’]

Updated food objects:

[0, 4, ‘F’, [0, 1000, 20]]

[0, 6, ‘F’, [1, 1000, 20]]

[1, 3, ‘F’, [2, 1000, 20]]

[1, 6, ‘F’, [3, 1000, 20]]

[2, 0, ‘F’, [4, 1000, 20]]

[2, 3, ‘F’, [5, 1000, 20]]

[3, 1, ‘F’, [6, 1000, 20]]

[3, 3, ‘F’, [7, 1000, 20]]

[3, 5, ‘F’, [8, 1000, 20]]

[3, 6, ‘F’, [9, 1000, 20]]

[5, 0, ‘F’, [10, 1000, 20]]

[5, 2, ‘F’, [11, 1000, 20]]

!!! MAIN: no more actors in the grid !!!

[‘_’, ‘_’, ‘_’, ‘_’, ‘F’, ‘O’, ‘F’]
[‘O’, ‘_’, ‘_’, ‘F’, ‘_’, ‘O’, ‘F’]
[‘F’, ‘O’, ‘O’, ‘F’, ‘_’, ‘_’, ‘O’]
[‘_’, ‘F’, ‘_’, ‘F’, ‘O’, ‘F’, ‘F’]
[‘O’, ‘_’, ‘O’, ‘_’, ‘O’, ‘O’, ‘_’]
[‘F’, ‘_’, ‘F’, ‘_’, ‘O’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘_’, ‘O’, ‘_’]

CYCle : 6 ————————-

MAIN LOOP: STOP = ‘N’, CONTINUE != ‘N’

Journal: uffmm.org,
ISSN 2567-6458, July 23, 2019 – May 13, 2020
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

Meanwhile I am beginning to combine elements of the python language with some applied ideas (in the last section the idea of cognitive entropy illustrated with the equalization of strings). In this section I extend the idea of a simple 2-dimensional virtual world and the construction of objects and their properties.

Remark: for a general help-information you can find a lot of helpful text directly on the python.org site: https://www.python.org/doc/.

SZENARIO

Motivation

Because we want to introduce step-wise artificial actors which can learn, show some intelligence, and can work in teams, we need a minimal virtual world to start (this virtual world is a placeholder for the real world later if applied to the real world (RW) by sensors and actors). Although there is a big difference between the real world and a virtual world a virtual world is nevertheless very helpful for introducing basic concepts. And, indeed, finally if you are applying to real world data you will not be able to do this without mathematical models which represent virtual structures. You will need lots of mappings between real and virtual and vice versa. Thus from a theoretical point of view any kind of virtual model will do, the question is only how ‘easily’ and how ‘good’ it fits.

Virtual World (VW) – General Assumptions

1. A 2-dimensional world as a grid together with a world clock CLCK. The clock produces periodic events which can be used to organize a timely order.

2. There is an x and y axis with the root (0,0) in the upper left corner

3. A coordinate (x,y) represents a position.

4. A position can be occupied by (i) nothing or (ii) by an object. Objects can be obstacles, energy objects (= Food), a virtual executive actor, or even more.

5. Only one object per position is allowed.

6. It is assumed that there are four main directions (headings): ‘North (N)’ as -Y, ‘East (E)’ as +X, ‘South (S)’ as +Y, and ‘West (W)’ as -X. There are additional for compound directions North-East, East-South, South-West, and South-West.

7. Possible movements are always in one of the mentioned directions from one cell to the adjacent cell. Movements will be blocked by obstacle objects or actor objects. In case of compound directions these are blocked to if the adjacent main directions are blocked

8. A sequence of movements realizes in the grid a path from one position to the next.

9. The size of the assumed grids is always finite. In a strict finite world the border of the grid blocks a movement. In a finite-infinite world the borders of the grid are connected in a way that the positions in the south lead to the position in the north and the positions in the east lead to the positions in the west, and vice versa. Therefor can a path in an finite-infinite world become infinite.

10. A grid G with its objects O will be configured at the beginning of an experiment. Without the artificial actors all objects are in a static world assumed to be permanent. In a dynamic world there can be a world function f_w inducing changes depending from the world clock. Between permanent and dynamic exists some intermediate changes: Food can be there but with varying amount of energy, as well as with an actor; he can posses properties which can change during its existence.

11. During an experiment possible changes in the world can happen through a world function f_w, if such a function has been defined. The other possible source for changes are artificial actors, which can act and which can ‘die’.

Remark: The basic idea of this kind of a virtual world I have got from a paper from S.W.Wilson from 1994 entitled ZCS: a zeroth level classifier system published in the Journal Evolutionary Computation vol. 2 number 1 pages 1-18. I have used this concept the first time in a lecture in 2012 (URL: https://www.doeben-henisch.de/fh/gbblt/node95.html). Although this concept looks at a first glance very simple, perhaps too simple, it is very powerful and allows very far reaching experiments (perhaps I can show some aspects from this in upcoming posts :-)).

ACTOR STORY

1. There is a human executive actor as a user who uses a program as an assisting actor by an interface with inputs and outputs.
2. The program tries to construct a 2D-virtual world in the format of a grid. The program needs the size of the grid as (m,n) = (number of columns, number of rows), which is here assumed by default as equal m=n. After the input the program will show the grid on screen.
3. Then the program will ask for the percentage of obstacles ‘O’ and energy objects (= food) ‘F’ in the world. Randomly the grid will be filled according to these values.
4. Then a finite number of virtual actors will be randomly inserted too, at least one.
5. After the completion of the virtual world grid a the list of all food objects as well as actors will be shown together with their individual Ids as well as additional properties.

IMPLEMENTATION

vw2b.py (main module)

gridHelper.py (import module)

EXERCISES

import copy as cp

The module ‘copy’ is important for cases where one wants to make a copy from an object with name ‘N’ and vaulue ‘V’ where the new name ‘N*’ should not be coupled to the old value ‘V’ but to some new value ‘V*’. Otherwise one has eventually many different new names ‘N1, N2, …, Nk’ but all are coupled to the same value ‘V’. Changing ‘V’ in connection with ohne Ni from all the new names wille be valid for all names. To stop such a synchrony between new names you have to use ‘copy’ like N*=copy.copy(N), or with the abbreviation ‘cp’: N*=cp.copy(N). In the source code there is one passage where this situation has become vivid.

def makeobjL(c,objL,mx,n):
ol=[]

In the following line list comprehension is used to collect all objects of type f==c in the matrix together with their (y,x) coordinates:
ol=[[y,x,f] for y in range(n) for x in range(n) for f in mx[y][x] if f==c]

Then for every such collected object the standard values of of the dictionary objL are appended to these objects by copying (!!! see above) these values. This is important because these values will all be changed afterwards independently from each other.
[ol[i].append(cp.copy(objL[c])) for i in range(len(ol))]

Now for every selected object with the  standard values an individual ID is computed and inserted at the first position of the property list.
for i in range(len(ol)):
ol[i][3][0]=i

return ol

olA, olA

With these operations we have now two independetn lists, one for food objects (olF), one for actor objects (olA) with individual values for each object. These can now individually  be managed during the upcoming simulation.

DEMO

PS C:\Users\gerd_2\code> python vw2b.py
Number of columns (= equal to rows!) of 2D-grid ?5

[‘_’, ‘_’, ‘_’, ‘_’, ‘_’]
[‘_’, ‘_’, ‘_’, ‘_’, ‘_’]
[‘_’, ‘_’, ‘_’, ‘_’, ‘_’]
[‘_’, ‘_’, ‘_’, ‘_’, ‘_’]
[‘_’, ‘_’, ‘_’, ‘_’, ‘_’]

Percentage (as integer) of obstacles in the 2D-grid?50
Number of objects :
12
Position :
4 0
Position :
0 2
Position :
3 2
Position :
2 0
Position :
1 0
Position :
3 2
Position :
1 4
Position :
2 4
Position :
2 4
Position :
2 0
Position :
1 0
Position :
3 0
New Matrix :

[‘_’, ‘_’, ‘O’, ‘_’, ‘_’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘O’, ‘_’, ‘_’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘_’]

Percentage (as integer) of Energy Objects (= Food) in the 2D-grid ?20
Number of objects :
5
Position :
0 4
Position :
3 4
Position :
1 0
Position :
3 4
Position :
0 1
New Matrix :

[‘_’, ‘F’, ‘O’, ‘_’, ‘F’]
[‘F’, ‘_’, ‘_’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘O’, ‘_’, ‘F’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘_’]

Percentage (as integer) of Actor Objects in the 2D-grid ?10
Number of objects :
2
Position :
4 3
Position :
0 2
New Matrix :

[‘_’, ‘F’, ‘A’, ‘_’, ‘F’]
[‘F’, ‘_’, ‘_’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘_’, ‘_’, ‘O’]
[‘O’, ‘_’, ‘O’, ‘_’, ‘F’]
[‘O’, ‘_’, ‘_’, ‘A’, ‘_’]

Objects as food

[0, 1, ‘F’, [0, 1000, 20]]
[0, 4, ‘F’, [1, 1000, 20]]
[1, 0, ‘F’, [2, 1000, 20]]
[3, 4, ‘F’, [3, 1000, 20]]

Objects as actor

[0, 2, ‘A’, [0, 1000, 5, 100]]
[4, 3, ‘A’, [1, 1000, 5, 100]]

STOP = ‘N’, CONTINUE != ‘N’
N