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Experiments
Here are 50 experiments with animat A0 and every experiment has 100 cycles (runs). As world do we use Grid GD5O4F1, which is one of the testgrids also used in the IEEE Africon2011 paper with the simulator software written in C++ by the students G.Abrami, M.Pfaff, and M.Struwe from the university of Applie Sciences Frankfurt am Main (Germany).
-->GD5O4F1 GD5O4F1 = !. . . O . ! ! ! !. . . . . ! ! ! !. O . . . ! ! ! !. . F O . ! ! ! !. O . . .
The grid W01 shows the probability distribution at the beginn of cycle 2 with the agent at (1,1).
-->[W01]=transprob(GD5O4F1)
W01 =
column 1 to 6
0.6666667 0.1111111 0. 0. 0. 0.1111111
0.1111111 0.4444444 0.1111111 0. 0. 0.1111111
0. 0.1111111 0.5555556 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.7777778 0.
0.1111111 0.1111111 0. 0. 0. 0.5555556
0.1111111 0.1111111 0.1111111 0. 0. 0.1111111
0. 0.1111111 0.1111111 0. 0. 0.
0. 0. 0.1111111 0. 0.1111111 0.
0. 0. 0. 0. 0.1111111 0.
0. 0. 0. 0. 0. 0.1111111
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
column 7 to 12
0.1111111 0. 0. 0. 0. 0.
0.1111111 0.1111111 0. 0. 0. 0.
0.1111111 0.1111111 0.1111111 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.1111111 0.1111111 0. 0.
0.1111111 0. 0. 0. 0.1111111 0.
0.2222222 0.1111111 0. 0. 0.1111111 0.
0.1111111 0.3333333 0.1111111 0. 0. 0.
0. 0.1111111 0.2222222 0.1111111 0. 0.
0. 0. 0.1111111 0.5555556 0. 0.
0.1111111 0. 0. 0. 0.5555556 0.
0. 0. 0. 0. 0. 0.
0.1111111 0.1111111 0.1111111 0. 0. 0.
0. 0.1111111 0.1111111 0.1111111 0. 0.
0. 0. 0.1111111 0.1111111 0. 0.
0. 0. 0. 0. 0.1111111 0.
0. 0. 0. 0. 0.1111111 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
column 13 to 17
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0.1111111 0. 0. 0. 0.
0.1111111 0.1111111 0. 0. 0.
0.1111111 0.1111111 0.1111111 0. 0.
0. 0.1111111 0.1111111 0. 0.
0. 0. 0. 0.1111111 0.1111111
0. 0. 0. 0. 0.
0.3333333 0.1111111 0. 0. 0.1111111
0.1111111 0.2222222 0.1111111 0. 0.
0. 0.1111111 0.5555556 0. 0.
0. 0. 0. 0.6666667 0.1111111
0.1111111 0. 0. 0.1111111 0.3333333
0.1111111 0.1111111 0. 0. 0.1111111
0. 0. 0. 0. 0.
0. 0.1111111 0.1111111 0. 0.
0. 0. 0. 0.1111111 0.1111111
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.1111111
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
column 18 to 23
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.1111111 0. 0. 0. 0. 0.
0.1111111 0. 0.1111111 0. 0. 0.
0. 0. 0.1111111 0. 0. 0.
0. 0. 0. 0.1111111 0. 0.
0.1111111 0. 0. 0.1111111 0. 0.1111111
0.4444444 0. 0. 0. 0. 0.1111111
0. 0. 0. 0. 0. 0.
0. 0. 0.5555556 0. 0. 0.
0. 0. 0. 0.7777778 0. 0.
0. 0. 0. 0. 0. 0.
0.1111111 0. 0. 0. 0. 0.6666667
0.1111111 0. 0.1111111 0. 0. 0.1111111
0. 0. 0.1111111 0. 0. 0.
column 24 to 25
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0.1111111 0.
0. 0.
0.1111111 0.1111111
0. 0.
0. 0.
0.1111111 0.
0.5555556 0.1111111
0.1111111 0.7777778
The matrix input gives the initial distribution of the agent in the 5 x 5 matrix.
--> INPUT=[1 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0]
INPUT =
1. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
0. 0. 0. 0. 0.
The function fieldprob() computes the 'flow' of probability through all cells for a number of RUNS (= cycles) assumed. As one can see in the example with RUNS=100 will the probability of a cell to become visited by the agent be nearly equal for all cells. This allows a forecast in the sense that an A0 agent will hit every cell in 100 cycles about 4.7 times.
-->RUNS=100, SHOW=0,[OUTPUT, STD]=fieldprob(W01,INPUT,RUNS, SHOW)
RUNS =
100.
SHOW =
0.
STD =
0.0178175
OUTPUT =
column 1 to 6
0.0476853 0.0476698 0.0476500 0. 0.0475930 0.0476806
column 7 to 12
0.0476627 0.0476373 0.0476097 0.0475913 0.0476651 0.
column 13 to 17
0.0476224 0.0475968 0.0475822 0.0476576 0.0476310
column 18 to 23
0.0475948 0. 0.0475535 0.0476542 0. 0.0475858
column 24 to 25
0.0475538 0.0475229
The following report shows a 'normal' experiment with 100 cycles. The HISTORY matrix gives the number of the cycles, then the kind of object found (-1 := boarder, 0 := empty space, 1 := object as obstacle, 2 := food). In all cases without food the animat is assumed to consume oer cycle 2 units of energy.
-->ANIMAT(1)=1, ANIMAT(2)=1,CLASSIFIERS=[],MODE=0,YYMAX=0, XXMAX=0,
S=2,GRID=GD5O4F1,DISTANCE=3,
RUNS=100,HISTORY=[],SHOW=0,[ANIMAT,HISTORY,GRID,FOOD]=liveR(ANIMAT,CLASSIFIERS,
MODE,YYMAX, XXMAX, S,GRID,DISTANCE, RUNS,HISTORY,SHOW)
HISTORY =
CYC OBJ X Y REW
-------------------------------
1. 0. 1. 2. - 2.
2. 0. 1. 3. - 4.
3. - 1. 0. 3. - 5.
4. 0. 1. 3. - 7.
5. 0. 1. 4. - 9.
6. 0. 1. 3. - 11.
7. - 1. 0. 4. - 12.
8. 0. 2. 2. - 14.
9. 0. 3. 3. - 16.
10. 0. 2. 2. - 18.
11. 0. 1. 3. - 20.
12. 0. 1. 3. - 22.
13. 0. 1. 4. - 24.
14. 1. 2. 3. - 25.
15. 1. 2. 3. - 26.
16. 0. 1. 5. - 28.
17. 0. 1. 4. - 30.
18. 0. 1. 5. - 32.
19. - 1. 0. 6. - 33.
20. 0. 2. 4. - 35.
21. 0. 2. 4. - 37.
22. 0. 3. 5. - 39.
23. 0. 4. 5. - 41.
24. 0. 5. 4. - 43.
25. - 1. 6. 5. - 44.
26. - 1. 6. 4. - 45.
27. - 1. 6. 3. - 46.
28. 0. 5. 4. - 48.
29. 0. 5. 5. - 50.
30. - 1. 6. 5. - 51.
31. - 1. 4. 6. - 52.
32. - 1. 6. 6. - 53.
33. - 1. 6. 4. - 54.
34. - 1. 5. 6. - 55.
35. 0. 5. 5. - 57.
36. - 1. 6. 6. - 58.
37. - 1. 4. 6. - 59.
38. 1. 4. 4. - 60.
39. - 1. 6. 4. - 61.
40. - 1. 6. 5. - 62.
41. 1. 4. 4. - 63.
42. - 1. 6. 6. - 64.
43. - 1. 6. 6. - 65.
44. - 1. 5. 6. - 66.
45. - 1. 5. 6. - 67.
46. - 1. 6. 6. - 68.
47. 0. 4. 5. - 70.
48. 0. 3. 5. - 72.
49. 0. 2. 4. - 74.
50. 0. 1. 4. - 76.
51. 0. 2. 4. - 78.
52. 0. 3. 3. - 80.
53. 0. 2. 2. - 82.
54. 0. 2. 2. - 84.
55. 0. 3. 3. - 86.
56. 0. 4. 3. - 88.
57. 0. 4. 3. - 90.
58. 0. 5. 4. - 92.
59. 1. 4. 4. - 93.
60. 0. 5. 5. - 95.
61. 0. 5. 4. - 97.
62. 0. 5. 4. - 99.
63. 1. 4. 4. - 100.
64. 0. 5. 4. - 102.
65. 1. 4. 4. - 103.
66. 0. 5. 4. - 105.
67. 0. 5. 3. - 107.
68. - 1. 6. 4. - 108.
69. 1. 4. 4. - 109.
70. 0. 4. 2. - 111.
71. 1. 4. 1. - 112.
72. 0. 4. 3. - 114.
73. 0. 5. 2. - 116.
74. - 1. 6. 2. - 117.
75. 1. 4. 1. - 118.
76. 0. 4. 2. - 120.
77. 0. 3. 2. - 122.
78. 1. 4. 1. - 123.
79. 0. 4. 2. - 125.
80. 0. 5. 2. - 127.
81. 0. 4. 3. - 129.
82. 2. 3. 4. 871.
83. 1. 4. 4. 870.
84. 1. 2. 5. 869.
85. 0. 4. 5. 867.
86. 0. 4. 5. 865.
87. 2. 3. 4. 1865.
88. 0. 3. 3. 1863.
89. 0. 3. 3. 1861.
90. 1. 2. 3. 1860.
91. 0. 3. 2. 1858.
92. 0. 2. 1. 1856.
93. 0. 2. 2. 1854.
94. 0. 3. 1. 1852.
95. 0. 4. 2. 1850.
96. 0. 3. 2. 1848.
97. 0. 2. 2. 1846.
98. 0. 2. 1. 1844.
99. 0. 1. 1. 1842.
100. 0. 2. 1. 1840.
This shows 50 repetitions of the experiment where every experiment has the same conditions and uses 100 cycles (= runs).
-->ANIMAT(1)=1, ANIMAT(2)=1,CLASSIFIERS=[],MODE=0,YYMAX=0, XXMAX=0,
S=2,GRID=GD5O4F1,DISTANCE=3, RUNS=100,HISTORY=[],SHOW=0,N=50,[FOODHITS,MEAN,
STD]=experiment(ANIMAT,CLASSIFIERS,MODE,YYMAX, XXMAX, S,GRID,DISTANCE,
RUNS,HISTORY,SHOW,N)
REWSUM/1000 = -0.000000
, TIMES FOOD = 0.000000, MEAN LENGHT = Nan
REWSUM/1000 = 4.000000
, TIMES FOOD = 4.000000, MEAN LENGHT = 9.750000
REWSUM/1000 = 5.000000
, TIMES FOOD = 5.000000, MEAN LENGHT = 9.400000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 50.000000
REWSUM/1000 = -0.000000
, TIMES FOOD = 0.000000, MEAN LENGHT = Nan
REWSUM/1000 = 3.000000
, TIMES FOOD = 3.000000, MEAN LENGHT = 20.666667
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 82.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 42.000000
REWSUM/1000 = 4.000000
, TIMES FOOD = 4.000000, MEAN LENGHT = 19.500000
REWSUM/1000 = 3.000000
, TIMES FOOD = 3.000000, MEAN LENGHT = 21.666667
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 28.000000
REWSUM/1000 = 5.000000
, TIMES FOOD = 5.000000, MEAN LENGHT = 12.400000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 15.000000
REWSUM/1000 = -0.000000
, TIMES FOOD = 0.000000, MEAN LENGHT = Nan
REWSUM/1000 = 3.000000
, TIMES FOOD = 3.000000, MEAN LENGHT = 32.333333
REWSUM/1000 = 9.000000
, TIMES FOOD = 9.000000, MEAN LENGHT = 10.666667
REWSUM/1000 = 3.000000
, TIMES FOOD = 3.000000, MEAN LENGHT = 32.000000
REWSUM/1000 = 4.000000
, TIMES FOOD = 4.000000, MEAN LENGHT = 21.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 46.000000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 13.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 27.000000
REWSUM/1000 = -0.000000
, TIMES FOOD = 0.000000, MEAN LENGHT = Nan
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 32.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 17.500000
REWSUM/1000 = 5.000000
, TIMES FOOD = 5.000000, MEAN LENGHT = 16.200000
REWSUM/1000 = 5.000000
, TIMES FOOD = 5.000000, MEAN LENGHT = 19.400000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 29.000000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 90.000000
REWSUM/1000 = 6.000000
, TIMES FOOD = 6.000000, MEAN LENGHT = 10.666667
REWSUM/1000 = -0.000000
, TIMES FOOD = 0.000000, MEAN LENGHT = Nan
REWSUM/1000 = 9.000000
, TIMES FOOD = 9.000000, MEAN LENGHT = 9.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 17.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 34.500000
REWSUM/1000 = 3.000000
, TIMES FOOD = 3.000000, MEAN LENGHT = 30.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 49.500000
REWSUM/1000 = 4.000000
, TIMES FOOD = 4.000000, MEAN LENGHT = 15.500000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 13.000000
REWSUM/1000 = 4.000000
, TIMES FOOD = 4.000000, MEAN LENGHT = 12.000000
REWSUM/1000 = 7.000000
, TIMES FOOD = 7.000000, MEAN LENGHT = 9.285714
REWSUM/1000 = 5.000000
, TIMES FOOD = 5.000000, MEAN LENGHT = 11.200000
REWSUM/1000 = 3.000000
, TIMES FOOD = 3.000000, MEAN LENGHT = 16.333333
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 23.500000
REWSUM/1000 = 7.000000
, TIMES FOOD = 7.000000, MEAN LENGHT = 14.285714
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 92.000000
REWSUM/1000 = 2.000000
, TIMES FOOD = 2.000000, MEAN LENGHT = 21.500000
REWSUM/1000 = 10.000000
, TIMES FOOD = 10.000000, MEAN LENGHT = 7.200000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 8.000000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 62.000000
REWSUM/1000 = 4.000000
, TIMES FOOD = 4.000000, MEAN LENGHT = 18.250000
REWSUM/1000 = 1.000000
, TIMES FOOD = 1.000000, MEAN LENGHT = 97.000000
STD =
2.4116215
MEAN =
2.98
FOODHITS =
0.
4.
5.
2.
0.
3.
1.
2.
4.
3.
2.
5.
1.
0.
3.
9.
3.
4.
2.
1.
2.
0.
1.
2.
5.
5.
1.
1.
6.
0.
9.
2.
2.
3.
2.
4.
2.
4.
7.
5.
3.
2.
7.
1.
2.
10.
1.
1.
4.
1.
Here is the minimally updated program used for these demonstrations.
//**************************************************************
// File: zcs_wood1_v2.sce
// Authors: Gerd Doeben-Henisch
// Version Start: April-14, 2010
//**********************************************************
// Update: April-14, 2010
// Updatet: April-15, 2010
// Update: April-16, 2010
// Update: April-17, 2010
// Update: April-20, 2010 (Change of coordinates: X for columns and Y for rows)
// Update: April-22, 2010 (Including the random number test procedures)
// Update: April-23, 2010 (A GRID with less food, Searching the distance to the
next food for an ANIMAT)
// Update: April-24, 2010
// Update: April-25, 2010 (New Testfields for the random case)
// Update: April-26, 2010
// Update: April-28, 2010
// Update: May-02, 2010
// Update: May-03, 2010 (Final Version of automatic generation of transition
table transprob(), transprob_tst())
//*****************************************
// Update May-29-2011-0835: Repeat tests; new test Grid GD5O4F1
//
//******************************************************************
// CONTENT: Necessary code for the ZCS system of Wilson (1994) and
// his wood1 environment
//
//***************************************************
// X-Y Coordinates
//
// scilab has the Y-axis 'inverted': scilab '1,2,...' = Usually '-1,-2,...'
// Thus we will write 'YI' for the scilab Y-axis
// Furthermore has the scilab matrix notation
// M(i,j) to be interpreted M(YI,X)
//
//************************************************
// 2-DICE PROBABILITIES
// Examples from Knuth (1981)(See lecture notes for details)
PROBABILITIES= [2 3 4 5 6 7 8 9 10 11 12; 1/36 1/18 1/12 1/9 5/36 1/6 5/36 1/9
1/12 1/18 1/36]
DICETEST1 =[2 4 10 12 22 29 21 15 14 9 6]
DICETEST2 = [4 10 10 13 20 18 18 11 13 14 13]
DICETEST3 = [3 7 11 15 19 24 21 17 13 9 5]
//***********************************
// BEHAVIOR FUNCTION WITH CLASSIFIERS
//
CLASSIFIERS = [
'11##############:0001-100';
'##11############:0010-100';
'####11##########:0011-100';
'######11########:0100-100';
'########11######:0101-100';
'##########11####:0110-100';
'############11##:0111-100';
'##############11:1000-100';
]
//**********************************
// GRID 'WOOD1'
//
// Y = r=1...15, X = c=1...55
// The Y-axis is from above (north) to bottom (south), the X-axis is from left
(west) to right (east)
// '.' := No Food; encoded '00'
// 'O' := Object (Rock); encoded '10'
// Attention: scilab assumes GRID(Y,X) columns first and then rows!
//********************************************************************
// DYNAMIC GRID GENERATION
// Using the wood1-structure as building blocks
//
// YYMAX := Max number of rows multiplied by 5
// XMAX := Max number of columnsXNtiplied by 5
function[GRID]=gridgen(YYMAX,XXMAX)
if (YYMAX < 1) | (XXMAX < 1) then printf("gridgen:ERROR WITH YYMAX,
XXMAX\n\n"), end
for k=1:5:5*YYMAX
i=k
for j=1:5*XXMAX, GRID(i+0,j)='.',end
i=k+1
for j=1:5:5*XXMAX,
GRID(i,j+0)='.'
GRID(i,j+1)='O'
GRID(i,j+2)='O'
GRID(i,j+3)='F'
GRID(i,j+4)='.'
end
i=k+2
for j=1:5:5*XXMAX,
GRID(i,j+0)='.'
GRID(i,j+1)='O'
GRID(i,j+2)='O'
GRID(i,j+3)='O'
GRID(i,j+4)='.'
end
i=k+3
for j=1:5:5*XXMAX,
GRID(i,j+0)='.'
GRID(i,j+1)='O'
GRID(i,j+2)='O'
GRID(i,j+3)='O'
GRID(i,j+4)='.'
end
i=k+4
for j=1:5*XXMAX, GRID(i+0,j)='.',end
end
endfunction
//********************************************************************
// DYNAMIC GRID GENERATION
// Using theextended wood1-structure as building block
//
// Allows less food cells in the world
//
// YYMAX := Max number of rows multiplied by 5
// XMAX := Max number of columnsXNtiplied by 5
// S := Inverse scaling to lower the density of food
function [GRID]=gridgen2(YYMAX,XXMAX,S)
if (YYMAX < 1) | (XXMAX < 1) then printf("gridgen:ERROR WITH YYMAX,
XXMAX\n\n"), end
r=0
for k=1:5:5*YYMAX
i=k
for j=1:5*XXMAX, GRID(i+0,j)='.',end
i=k+1
for j=1:5:5*XXMAX,
GRID(i,j+0)='.'
GRID(i,j+1)='O'
GRID(i,j+2)='O'
if (S>0) & (r==S) then GRID(i,j+3)='F', r=0, else GRID(i,j+3)='O',
r=r+1,end
GRID(i,j+4)='.'
end
i=k+2
for j=1:5:5*XXMAX,
GRID(i,j+0)='.'
GRID(i,j+1)='O'
GRID(i,j+2)='O'
GRID(i,j+3)='O'
GRID(i,j+4)='.'
end
i=k+3
for j=1:5:5*XXMAX,
GRID(i,j+0)='.'
GRID(i,j+1)='O'
GRID(i,j+2)='O'
GRID(i,j+3)='O'
GRID(i,j+4)='.'
end
i=k+4
for j=1:5*XXMAX, GRID(i+0,j)='.',end
end
endfunction
//********************************************************************
// TESTGRIDS WITH OBJECTS
//
//
GD3O2F1=['.' '.' '.'; 'O' 'O' '.'; '.' 'F' '.' ]
GD3O2F1_2=['.' '.' '.'; '.' 'O' 'O'; '.' 'F' '.' ]
GD3O2F1_3=['.' '.' '.'; '.' 'O' '.'; '.' 'F' '.' ]
GD5O4F1=['.' '.' '.' 'O' '.'; '.' '.' '.' '.' '.';'.' 'O' '.' '.' '.'; '.' '.'
'F' 'O' '.';'.' 'O' '.' '.' '.']
//*************************************************
// AUTOMATIC GENERATION OF TRANSITION TABLE
// Takes every cell of a given GRID and computes the transition probabilities
// with regard to every possible direction 1 ... 8
//
function [TGRID]=transprob(GRID)
[r,c]=size(GRID)
TGRID=zeros()
N=r*c
MAX=9
Y=0
while(Y<N)
for y=1:r,
for x=1:c, Y=Y+1
// printf("\n---------------Y = %d----------------------\n\n",Y)
m=1
//Catch wrong indices and evaluate cases
if (y-1)<1 then m=m+1, elseif GRID(y-1, x) == 'O' then
TGRID(Y,(y-1)*c+x)=0, m=m+1,else TGRID(Y,(y-1-1)*c+x)=1/MAX, end
if (y-1)<1|(x+1)>c then m=m+1,elseif GRID(y-1, x+1) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1-1)*c+x+1)=1/MAX, end
if (x+1)>c then m=m+1,elseif GRID(y, x+1) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1)*c+x+1)=1/MAX, end
if (y+1)>r|(x+1)>c then m=m+1,elseif GRID(y+1, x+1) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1+1)*c+x+1)=1/MAX, end
if (y+1)>r then m=m+1,elseif GRID(y+1, x) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1+1)*c+x)=1/MAX, end
if (y+1)>r|(x-1)<1 then m=m+1,elseif GRID(y+1, x-1) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1+1)*c+x-1)=1/MAX, end
if (x-1)<1 then m=m+1,elseif GRID(y, x-1) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1)*c+x-1)=1/MAX, end
if (y-1)<1|(x-1)<1 then m=m+1,elseif GRID(y-1, x-1) == 'O' then
TGRID(Y,(y-1)*c+x)=0,m=m+1,else TGRID(Y,(y-1-1)*c+x-1)=1/MAX, end
if GRID(y, x)=='O' then for z=1:N,TGRID(Y,z)=0,end
else TGRID(Y,Y)=m/MAX, end
end //x
end //y
end //Y
endfunction
//*************************************************
// AUTOMATIC GENERATION OF TRANSITION TABLE
// Takes every cell of a given GRID and computes the transition probabilities
// with regard to every possible direction 1 ... 8
//
function [TGRID]=transprob_tst(GRID)
[r,c]=size(GRID)
TGRID=zeros()
N=r*c
MAX=9
Y=0
while(Y<N)
for y=1:r,
for x=1:c, Y=Y+1
printf("\n---------------Y = %d----------------------\n\n",Y)
m=1
//Catch wrong indices and evaluate cases
if (y-1)<1 then printf(" __BORDER__"), m=m+1, elseif GRID(y-1, x) ==
'O' then printf(" __OBJECT__"),TGRID(Y,(y-1)*c+x)=0, m=m+1,else printf("
__MOVE__"),TGRID(Y,(y-1-1)*c+x)=1/MAX, printf("(y,x) = (%d,%d) with m = %d
TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1-1)*c+x, TGRID(Y,(y-1-1)*c+x)), end
if (y-1)<1|(x+1)>c then printf(" __BORDER__"), m=m+1,elseif GRID(y-1,
x+1) == 'O' then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else
printf(" __MOVE__"),TGRID(Y,(y-1-1)*c+x+1)=1/MAX, printf("(y,x) = (%d,%d) with m
= %d TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1-1)*c+x+1,TGRID(Y,(y-1-1)*c+x+1)),
end
if (x+1)>c then printf(" __BORDER__"), m=m+1,elseif GRID(y, x+1) == 'O'
then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else printf("
__MOVE__"),TGRID(Y,(y-1)*c+x+1)=1/MAX, printf("(y,x) = (%d,%d) with m = %d
TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1)*c+x+1,TGRID(Y,(y-1)*c+x+1)), end
if (y+1)>r|(x+1)>c then printf(" __BORDER__"), m=m+1,elseif GRID(y+1,
x+1) == 'O' then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else
printf(" __MOVE__"),TGRID(Y,(y-1+1)*c+x+1)=1/MAX, printf("(y,x) = (%d,%d) with m
= %d TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1+1)*c+x+1,TGRID(Y,(y-1+1)*c+x+1)),
end
if (y+1)>r then printf(" __BORDER__"), m=m+1,elseif GRID(y+1, x) == 'O'
then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else printf("
__MOVE__"),TGRID(Y,(y-1+1)*c+x)=1/MAX, printf("(y,x) = (%d,%d) with m = %d
TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1+1)*c+x,TGRID(Y,(y-1+1)*c+x)), end
if (y+1)>r|(x-1)<1 then printf(" __BORDER__"), m=m+1,elseif GRID(y+1,
x-1) == 'O' then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else
printf(" __MOVE__"),TGRID(Y,(y-1+1)*c+x-1)=1/MAX, printf("(y,x) = (%d,%d) with m
= %d TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1+1)*c+x-1,TGRID(Y,(y-1+1)*c+x-1)),
end
if (x-1)<1 then printf(" __BORDER__"), m=m+1,elseif GRID(y, x-1) ==
'O' then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else printf("
__MOVE__"),TGRID(Y,(y-1)*c+x-1)=1/MAX, printf("(y,x) = (%d,%d) with m = %d
TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1)*c+x-1,TGRID(Y,(y-1)*c+x-1)), end
if (y-1)<1|(x-1)<1 then printf(" __BORDER__"), m=m+1,elseif GRID(y-1,
x-1) == 'O' then printf(" __OBJECT__"), TGRID(Y,(y-1)*c+x)=0,m=m+1,else
printf(" __MOVE__"),TGRID(Y,(y-1-1)*c+x-1)=1/MAX, printf("(y,x) = (%d,%d) with m
= %d TG(Y,Y) = (%d,%d) = %f\n",y,x,m,Y,(y-1-1)*c+x-1,TGRID(Y,(y-1-1)*c+x-1)),
end
if GRID(y, x)=='O' then for z=1:N,TGRID(Y,z)=0,end
else TGRID(Y,Y)=m/MAX, end
end //x
end //y
end //Y
endfunction
//**********************************************
// SOME TEST RESULTS
// Mean and StandardDeviation directly by scilab:
//
// M=[],for i=1:6, M(i,1)=mean(D2RESULTS0(i,:)), M(i,2)=
st_deviation(D2RESULTS0(i,:)), end
// Extracting the MAXima of a run
// MAXD2=max(D2RESULTS0,"c")
// Extracting the 1st row out of the results
// D2R30=D2RESULTS0([1],[:])
// Starting a counter for the frequencies
// F30=zeros(1,MAXD2(1,1)+1)
// F40=zeros(1,MAXD2(2,1)+1)
// Computing the frequency of the values of a run
// for i=1:c, F30(1,D2R30(1,i)+1)=F30(1,D2R30(1,i)+1)+1,end
// for i=1:c, F40(1,D2R40(1,i)+1)=F40(1,D2R40(1,i)+1)+1,end
// F30 =[12 8 2 3] := 12 x 0, 8 x 1, ...
// Compute the means by using the values in T and the frequencies in F
// N=sum(F), MEAN=0, for i=1:length(T), MEAN=MEAN+(F(1,i)*T(1,i)),end;
MEAN=MEAN/N
// DIFFs=[], for i=1:length(F30), DIFFs(1,i)=(i-1)-MEAN,end
// DIFs2=[],for i=1:length(F30), DIFs2(1,i)=DIFFs(1,i)^2,end
// Attention: the index of the array FX30 as 'i-1' is the observed value!
// SAQs=[],for i=1:length(F30), SAQs(1,i)=F30(1,i)*DIFs2(1,i),end
// standard deviation
// N=sum(F30),s=sqrt(sum(SAQs)/(N-1))
//**********************************
// RANDOM TEST WORLD FOR BENCHMARKING
//
// This world has one food cell with 'F' in the center and
// has a fixed diameter d according to the distance which
// shall be explored.
function[GRID]=testgrid(DISTANCE)
D=DISTANCE
//Generate normal empty space '.'
for j=1:(2*D)+1,
for i=1:(2*D)+1
GRID(i,j)='.',
end
end
//Locate the food in the center
GRID(D+1,D+1)='F'
endfunction
//*************************************************
// ANIMAT STRUCTURE
Xanimat = 3
Yanimat = 5
RewTotal = 0
RewActual = 0
ANIMAT = list(Xanimat, Yanimat, RewTotal, RewActual)
//****************************************************
// HISTORY
// Collecting data during one life-cycle
// 1 := Cycle number k
// 2 := Object before move
// 3 := X-Pos
// 4 := Y-Pos
// 5 := REWTotal
HISTORY =[]
//***************************************************
// Function to decode the content of a cell
function[CELLVALUE]=contdecode(YI,X,GRID,SHOW)
[YMAX, XMAX]=size(GRID)
if (SHOW==1) then printf("contdecode: SIZE OF GRID (X,Y) =
(%d,%d)\n",XMAX,YMAX),end
CELLVALUE=[]
if (SHOW==1) then printf("contdecode: YI= %d, X= %d\n",YI,X),end
if (X > XMAX) | (X < 1) | (YI > YMAX) | (YI <1) then CELLVALUE='BB'
elseif GRID(YI,X) =='.' then CELLVALUE='00'
elseif GRID(YI,X) =='O' then CELLVALUE='10'
elseif GRID(YI,X) =='F' then CELLVALUE='11'
else printf("contdecode:ERROR AT CELL =( %d,%d)\n",X,YI)
end
endfunction
//*************************************************
// SENSING ENVIRONMENT
//
// Loocking to the 'north' the Animat can see the content of all it's
neighbouring cells clockwise around.
// The encoding is as follows:
// '.' := Empty space encoded '00'
// 'O' := Object Rock encoded '10'
// 'F' := Object Food encoded '11'
//
// Different to Wilson (1994) we do not assume an 'infinite finite' world by 15
rows and 55 columns
// but a finite world with dynamic shape. This allows to investigate the effect
of GRID size onto different parameters.
//
function [SENSINPUT,INPUT]=animsens(YI,X,GRID,SHOW)
//Having a 'number' version of the input
SENSINPUT=[]
//Having a 'sring' version of the input
INPUT=""
//The cells will be searched from 'north' in clockwise order
// The coordinates below are relativ to the actual position of the ANIMAT
P=[-1,0; -1,1; 0,1; 1,1; 1,0; 1, -1; 0,-1; -1,-1 ]
[r,c]=size(P)
for j=1:r, SENSINPUT(j)=contdecode(YI+P(j,1),X+P(j,2),GRID),
INPUT=INPUT+string(SENSINPUT(j))
end
if (SHOW==1) then
printf("Sens Input: %s\n\n",INPUT)
end
endfunction
//*************************************************
// RANDOM MOVEMENT GENERATOR
// Generates movements based on uniform random numbers from 0 ... 8
//
// XO, YO := old X-Y-Position
// XN, XN := new X-Y-Position
function [XN,YN] =mover(XO, YO, GRID)
R=floor(9*rand())
//printf("mover:RANDOM NUMBER R= %d\n\n",R)
//The cells will be searched from 'north' in clockwise order
if R == 0 then XN=XO, YN=YO
elseif R == 1 then YN=YO-1, XN=XO
elseif R==2 then YN=YO-1, XN=XO+1
elseif R==3 then YN=YO, XN=XO+1
elseif R==4 then YN=YO+1, XN=XO+1
elseif R==5 then YN=YO+1, XN=XO
elseif R==6 then YN=YO+1, XN=XO-1
elseif R==7 then YN=YO, XN=XO-1
elseif R==8 then YN=YO-1, XN=XO-1
else printf("mover:ERROR WITH MOVEMENT AT = (XO, YO) = ( %d,%d)\n",XO,YO)
end
endfunction
//*************************************************
// FOOD DISTANCE EVALUATOR
// Starting the search at the actual position of the ANIMAT
// the search searches all cells with distance=i in a
// clockwise order. If it finds food cells these will
// be 'tracked' and the distance is is fixed to i.
//
// Attention: If the ANIMAT is located near the boarder of the GRID one
// has to 'neutralize' thoses values which point 'beyond' the GRID
function [FDISTANCE,GLIST]=foodsearch(ANIMAT, GRID,SHOW)
if(SHOW==1) then
printf("foodsearch: START AT (%d,%d)\n\n",ANIMAT(1),ANIMAT(2)),end
//The cells will be searched from 'north' in clockwise order
// GOALFLAG indicates having found a food cell
// FDISTANCE measures the distance
// K is an index to list the goal coordinates
// GLIST collects the coordinates of the goals
[row,col]=size(GRID)
if(SHOW==1) then printf("row = %d, col = %d\n",row,col),end
GOALFLAG=0
FDISTANCE=0
X=ANIMAT(1),YI=ANIMAT(2)
GLIST=[]
K=1
if(SHOW==1) then
printf("foodsearch: 1\n\n"),end
L=1
while(GOALFLAG==0)| (2*L>floor(sqrt(row^2+col^2)))
if(SHOW==1) then
printf("foodsearch: 2, L=%d\n\n",L),end
for j=-L:L, for i=-L:L,
if(SHOW==1) then
if(X+i <1)|(YI+j <1)| (X+i >col) |(YI+j>row) then printf("BEYOND\n"),
else printf("foodsearch: 3 (YI,X) = (%d,%d) =
%s\n\n",YI+j,X+i,GRID(YI+j,X+i)),end
end
if(X+i >1)&(YI+j >1)& (X+i <col) &(YI+j<row) & GRID(YI+j,X+i)=='F' then
GOALFLAG=1, FDISTANCE=L,GLIST(K,1)=X+i, GLIST(K,2)=YI+j,K=K+1,end
end // FOR
end //FOR
L=L+1
end //WHILE
endfunction
//*************************************************
// MOVEMENT EVALUATION
// DEPENDING FROM CONTENT OF NEW PROPOSED POSITION SELECT ACTION
//
// Borderline := -3 (negative reward)
// Object := -2 (negative reward)
// Empty cell := -1 (move into it, but looses energy)
//// Food := (move into it, reward +1000)
//
// Borderline := XXMAX = YYMAX = 1 or YYMAX or XXMAX
//
// CONT := Decoded content
// GRID := Assumed GRID
// REW := Reward inferred
// ACT := No Move as 0, Move as 1
function [REW,ACT,OBJ] =moveeval(CONT, GRID,SHOW)
if CONT == 'BB' then REW=-1, ACT=0, OBJ=-1
elseif CONT == '00' then REW=-2, ACT=1, OBJ=0
elseif CONT == '10' then REW=-1, ACT=0, OBJ=1
elseif CONT == '11' then REW=1000, ACT=1, OBJ=2
else printf("moveeval:ERROR WITH MOVEEVAL\n\n")
end
if (SHOW==1) then
printf("moveeval:REWARD = %d, ACTION = %d\n\n",REW,ACT)
end
endfunction
//*************************************************
// UPDATE ANIMAT
//
// Either keep position or move.
// Summarize reward
function [ANIMAT] = updateAnim(ANIMAT,REW,ACT,XN,YN,SHOW)
if ACT == 1 then ANIMAT(1) = XN, ANIMAT(2) = YN, ANIMAT(4) = REW,
ANIMAT(3)=ANIMAT(3)+REW
else ANIMAT(4) = REW, ANIMAT(3)=ANIMAT(3)+REW
end
if (SHOW==1) then
printf("update ANIMAT: A-POS = (%d,%d), A-REW = %d, A-REW-SUM =
%d\n\n",ANIMAT(1), ANIMAT(2), ANIMAT(4), ANIMAT(3))
end
endfunction
//***************************************************
// Function to fetch the conditions out of the classifier set
//
function [CONDITIONS]=getCondition(CLASSIFIERS)
[r,c]=size(CLASSIFIERS)
CONDITIONS=""
for j=1:r
T=tokens(CLASSIFIERS(j,1),[':']), CONDITIONS(j)=string(T(1,1))
end
endfunction
//***************************************************
// Get set of matching conditions
function [MATCHSET]=getCondition(CONDITIONS, INPUT)
[r,c]=size(CONDITIONS)
//...to be completed...
endfunction
//***************************************************
// SIMPLE AUTOMATED TEST FRAMEWORK FOR ZCS
//
// Automatic testing for a certain GRID with given numers of runs
//
// ANIMAT := Structure OF ANIMAT system
// HISTORY := Protocol of movements and cumulated rewards
// GRID := Content of environment
// MODE := random (=0), non-random (=1)
// YYMAX := Number of rows times 5
// XXMAX := Number of columns times 5
// RUNS := How many cycles the simulation shall run. 1 cycle means one action
// S := scaling factor in the sparse food case
// GRID := Fixed GRID from external source
// DISTANCE := General distance from food in test field
function [ANIMAT,HISTORY,GRID,FOOD]=liveR(ANIMAT,CLASSIFIERS,MODE,YYMAX, XXMAX,
S,GRID,DISTANCE, RUNS,HISTORY,SHOW)
if (RUNS<0) then error('RUNS is not specified'),end
//GENERATE FIXED TEST GRID
if (S==-1) then [GRID]=testgrid(DISTANCE),
//GENERATE dynamic GRID
elseif (S == 0) then [GRID]=gridgen(YYMAX, XXMAX),
//GENERATE dynamic GRID with sparse food
elseif (S == 1) then [GRID]=gridgen2(YYMAX,XXMAX,S),
// USE external GRID
else S =2,
end
//SET GLOBAL VARIABLES TO ZERO
HISTORY=[]
ANIMAT(3)=0
f=1 //Index to store in which cycle food has been found
FOOD=[] //Storage for differences
FOLD=0
FNEW=0
k=1
while(k < RUNS+1),
if (SHOW==1) then
printf("-----CYCLE
%d---------------------------------------------------\n\n",k)
end
[YMAX,XMAX]= size(GRID)
//printf("DIMENSION OF GRID - ROWS = %d, COLUMNS = %d\n\n",ZMAX,XMAX)
// (1) Get actual position (XO,YO) from Animat
// Is given with argument ANIMAT(1) = rows and ANIMAT(2) = columns
XO= ANIMAT(1)
YO= ANIMAT(2)
if (SHOW==1) then
printf("ACTUAL POSITION =(XO,YO) = (%d, %d)\n\n",XO,YO)
end
// Get SENSINPUT as string: INPUT
if(MODE==1) then
[SENSINPUT,INPUT]=animsens(YO,XO,GRID,SHOW)
end
// (2) Generate new move (XN,YN)
[XN,YN] =mover(XO, YO, GRID)
if (SHOW==1) then
printf("NEW POSITION PROPOSED=(XN,YN)= (%d, %d)\n\n",XN,YN),end
// (3) Decode the content of new position
[CELLVALUE]=contdecode(YN,XN,GRID,SHOW)
if (SHOW==1) then,
printf("CONTENT OF NEW POSITION= %s\n\n",CELLVALUE),end
// (4) Depending from content finalize move
[REW,ACT,OBJ] =moveeval(CELLVALUE, GRID,SHOW)
// (5) Update ANIMAT
[ANIMAT] = updateAnim(ANIMAT,REW,ACT,XN,YN,SHOW)
if (REW==1000) then FNEW=k,FOOD(f,1)=FNEW-FOLD, FOLD=FNEW, f=f+1, end
if (SHOW==1) & (REW==1000) then printf("FOOD in CYCLE = %d\n\n",k),end
// Update PATHHISTORY
[r,c]=size(HISTORY)
//INDEX
HISTORY(r+1,1)=k
//CONTENT
HISTORY(r+1,2)=OBJ
// POSITION
HISTORY(r+1,3)=XN, HISTORY(r+1,4)=YN
//REWARDTOTAL
HISTORY(r+1,5)=ANIMAT(3)
//Update while loop
k=k+1,
end
MEAN=mean(FOOD)
printf("REWSUM/1000 = %f\n, TIMES FOOD = %f, MEAN LENGHT =
%f\n\n",ceil((ANIMAT(3)/1000)),length(FOOD),MEAN)
endfunction
//***************************************************
// SIMPLE AUTOMATED TEST FRAMEWORK FOR ZCS
// WITH REPETITION
//
// ANIMAT := Structure OF ANIMAT system
// HISTORY := Protocol of movements and cumulated rewards
// GRID := Content of environment
// MODE := random (=0), non-random (=1)
// YYMAX := Number of rows times 5
// XXMAX := Number of columns times 5
// RUNS := How many cycles the simulation shall run. 1 cycle means one action
// S := scaling factor in the sparse food case
// GRID := Fixed GRID from external source
// DISTANCE := General distance from food in test field
// N:= Number of repetitions of an experiment with RUNS-many actions
// FOODHITS := List of food hits per repetition
// MEAN := statistical mean
// STD := standard deviation
function [FOODHITS,MEAN, STD]=experiment(ANIMAT,CLASSIFIERS,MODE,YYMAX, XXMAX,
S,GRID,DISTANCE, RUNS,HISTORY,SHOW,N)
FOODHITS=[]
for i=1:N,
[ANIMAT,HISTORY,GRID,FOOD]=liveR(ANIMAT,CLASSIFIERS,MODE,YYMAX, XXMAX,
S,GRID,DISTANCE, RUNS,HISTORY,SHOW),
[r,c]=size(FOODHITS),
//INDEX
FOODHITS(r+1,1)=length(FOOD),
end
MEAN=mean(FOODHITS)
STD=st_deviation(FOODHITS)
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// N := Numbers of psyeudo random numbers to generate
// EQUISUMS := counting each number r_i within N
// SHOW := Flag, if '1' then print random numbers, otherwise not
// C := Number of categories +1
function [EQUISUMS] =countingrands(N,C,SHOW)
// Matrix for counting the C-many different events
EQUISUMS=zeros(1,C)
//Loop to generate pseudo random numbers
i=0
while (i<N),
R=floor(C*rand())
//PRINT 'on demand'
if (SHOW==1) then printf("countingrands:RANDOM NUMBER R= %d\n",R)
end
//Counting occurences of different numbers
for j=1:C,
if (R==j-1) then EQUISUMS(j)= EQUISUMS(j)+1,end
end
i=i+1
end
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// N := Numbers of psyeudo random numbers to generate
// EQUISUMS := counting each number r_i within N
// SHOW := Flag, if '1' then print random numbers, otherwise not
// C := Number of categories +1
function [EQUISUMS] =countingMatrix1(N,C,SHOW)
// Matrix for counting the C-many different events
EQUISUMS=zeros(1,C)
//Generate pseudo random numbers
RANDMATRIX=rand(1,N)
//Counting occurences of different numbers
for i=1:N
RANDMATRIX(1,i)=floor(RANDMATRIX(1,i)*C)
for j=1:C,
if (RANDMATRIX(1,i)==j-1) then EQUISUMS(j)= EQUISUMS(j)+1,end
end
end
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// N := Numbers of psyeudo random numbers to generate
// EQUISUMS := counting each number r_i within N
// SHOW := Flag, if '1' then print random numbers, otherwise not
// C := Number of categories +1
function [EQUISUMS] =countingMatrix2(N,C,SHOW)
// Matrix for counting the C-many different events
RANDMATRIX=zeros(1,N)
EQUISUMS=zeros(1,C)
//Generate pseudo random numbers
RANDMATRIX=grand(1,N,'uin',0,C-1)
printf("\nSeed after %d\n",rand('seed'))
//Counting occurences of different numbers
for i=1:N
for j=1:C,
if (RANDMATRIX(1,i)==j-1) then EQUISUMS(j)= EQUISUMS(j)+1,end
end
end
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// Computing the differences between theoreticalyl expected and observed values
function [DIFFSUMS] =sumdifferences(N,C,EQUISUMS)
DIFFSUMS=zeros(1,C)
for i=1:C, DIFFSUMS(i)=(N/C)-EQUISUMS(i),
DIFFSUMS(i)=DIFFSUMS(i)/(N/(C/100)),end
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// Computing the Chi-square statistics assuming equally distributed
probabilities
function [CHISQUARE] =chisquare1(N,C,EQUISUMS,SHOW)
np=(1/C)*N
S=0
for i=1:C,
if (SHOW==1) then
printf(" i= %d, Observed= %d, Expected = %f\n",i,EQUISUMS(i), np),
printf(" i= %d,Diff= %f,Diff-Square = %f\n",i,EQUISUMS(i)- np, (EQUISUMS(i)-
np)^2),
printf(" i= %d,Quotient = %f\n",i,((EQUISUMS(i)- np)^2)/np), end
S=S+((EQUISUMS(i)-np)^2)/np,
end
CHISQUARE=S
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// Computing the Chi-square statistics assuming different probabilities
// PROBABILITIES := Array of different probabilities
function [CHISQUARE] =chisquare1b(N,C,EQUISUMS,SHOW)
[r,c]=size(EQUISUMS)
if(c <> C) then printf("chisquare1b: c and C DIFFERENT!!!\n\n"),end
PROBABILITIES=zeros(r,c)
for i=1:c, PROBABILITIES(1,i)=1/C,end
S=0
for i=1:c
S=S+((EQUISUMS(i)-N*PROBABILITIES(1,i))^2)/(N*PROBABILITIES(1,i)),end
CHISQUARE=S
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR SIMULATING TWO DICES
//
// N := Numbers of psyeudo random numbers to generate
// EQUISUMS := counting each number r_i within N
// SHOW := Flag, if '1' then print random numbers, otherwise not
// C := Number of overall categories +1
// MAXDICE := MAximum of onbe dice
function [EQUISUMS] =counting2rands(N,SHOW,MAXDICE)
// Matrix for counting the C-many different events
EQUISUMS=zeros(1,(MAXDICE*2)-1)
//Loop to generate pseudo random numbers
i=0
while (i<N),
R1=floor(MAXDICE*rand())+1
R2=floor(MAXDICE*rand())+1
X = R1+R2
//PRINT 'on demand'
if (SHOW==1) then printf("countingrands:RANDOM NUMBERs R1= %d, R2=%d,
X=%d\n",R1,R2,X)
end
//Counting occurences of different numbers
for j=2:MAXDICE*2,
if (X==j) then EQUISUMS(j-1)= EQUISUMS(j-1)+1,
end
end
i=i+1
end
endfunction
//*************************************************
// RANDOM NUMBER GENERATOR TESTs
// Testing the quality of the produced pseudo-random numbers
// according to some procedures described by Knuth 1989.
//
// Computing the Chi-square statistics assuming different probabilities
// PROBABILITIES := Array of different probabilities
function [CHISQUARE] =chisquare2(N,EQUISUMS,PROBABILITIES,SHOW)
[RR,CC]=size(PROBABILITIES)
[r,c]=size(EQUISUMS)
if (c <> CC) then printf("chisquare2:EQUISUMS and PROBABILITIES have
DIFFERENT columns\n\n"),end
CHISQUARE=0
S=0
for i=1:CC,
if (SHOW==1) then
printf(" i= %d, Observed= %d, Expected = %f\n",i,EQUISUMS(i),
N*PROBABILITIES(2,i)),
printf(" i= %d,Diff= %f,Diff-Square = %f\n",i,EQUISUMS(i)-
N*PROBABILITIES(2,i), (EQUISUMS(i)- N*PROBABILITIES(2,i))^2),
printf(" i= %d,Quotient = %f\n",i,((EQUISUMS(i)-
N*PROBABILITIES(2,i))^2)/N*PROBABILITIES(2,i)), end
S=S+((EQUISUMS(i)-N*PROBABILITIES(2,i))^2)/(N*PROBABILITIES(2,i)),end
CHISQUARE=S
endfunction
//*************************************************
// CELLDISTANCE
// Measured in Manhattan Distance
// Not usable for movements because movements can happen as diagonal moves too!
//
// V1 =(X1,Y1)
// V2 = (X2,Y2)
function [CELLDIST] =celldistance(V1,V2)
CELLDIST=( abs(V1(1,1)-V2(1,1)) + abs(V1(1,2)-V2(1,2)) )
endfunction
//*********************************************************
//Some common constants
//Conversion of radiants into grade
grad=(2*%pi)/360
//Conversion of radiants into degrees
igrad=360/(2*%pi)
//***************************************************
//Function to compute the sum of a vector v and the i-th row of a matrix M
function[M]=vecAddMi(M,i,v)
//Test whether the length of v is equal to the columns of M
[r,c]=size(M);
if l <> c then error('SIZE of VECTOR DOES NOT MATCH MATRIX'),
else
for j=1:c, M(i,j) = M(i,j)+v(j),
end
end
endfunction
//***************************************************
//Function to compute the difference of a vector v and the i-th row of a matrix
M
function[M]=vecSubMi(M,i,v)
//Test whether the length of v is equal to the columns of M
l=length(v);
[r,c]=size(M);
if l <> c then error('SIZE of VECTOR DOES NOT MATCH MATRIX'),
else
for j=1:c, M(i,j) = M(i,j)-v(j),
end
end
endfunction
//***************************************************
//Function to compute the product of two vectors v,w
function[u]=vecprod(v,w)
lv=length(v);
lw=length(w);
u=0
if lv <> lw then error('DIFFERENT LENGTH OF VECTORS');
else for i=1:lv, u=u + (v(i)*w(i));
end
end
endfunction
//***************************************************
//Function to compute the projection of a vector v onto a line with angle theta
//angle in degree
function[y]=vecproj(v,t)
//Convert radiants into degree
t=t/igrad
y=norm(v)*cos(t)
endfunction
//***************************************************
//Function to compute the projection of a vector v onto a line with line w
function[y]=vecprojL(v,w)
y=vecprod(v,w)/norm(w)
endfunction
//*************************************************************
//Function to compute the angle of two vectors v,w in 360 degree
//Vectors v,w are column or row vectors
function[y]=cosvec(v,w)
y=vecprod(v,w)/(norm(v)*norm(w))
//The result in y is the cos-value. We need the invers of this
y=acos(y)
//The inverse is given in radiants. We want the conversion to degrees
y=y/grad
endfunction
//End of function cosvect ******************************************
//*******************************************************
//Function to multiply a vector v with a quadratic matrix M generating a vector
u
function[u]=vmprod(v,M)
[r,c]=size(M)
lv=length(v)
u=zeros(1,lv)
if (r <> lv) then error('sizes of vector and matrix do not match')
else
for i=1:c //Following the columns of matrix
for j=1:r //Working through all elements of v and rows of M
u(j)=u(j) + (v(i) *M(i,j))
end
end
end
endfunction
//*******************************************************
//Function to multiply a vector v with a selectable matrix M generating a vector
u
function[u]=vmprod2(M,v)
[r,c]=size(M)
lv=length(v)
u=zeros(1,lv)
if (r <> lv) then error('sizes of vector and matrix do not match')
else
for j=1:r //Following the rows of matrix
for i=1:c //Working through all elements of v and columns of M
u(j)=u(j) + (v(i) *M(j,i))
end
end
end
endfunction
//*******************************************************
//Macro for the usage of vmprod2()
//
function [OUTPUT]=vmprod2makro(WEIGHT,INPUT,RUNS)
for i=1:RUNS,
[OUTPUT]=vmprod2(WEIGHT,INPUT),
INPUT = OUTPUT,
end
endfunction
//*******************************************************
// DYNAMIC PROBABILITY FIELD
// Function for the repeated usage of vmprod2()
//
// INPUT:= Initial Probability Distribution of the Field (agent =1)
// WEIGHT := As weight is used the probability distribution computed with
transprob() giving the distribution
// of probabilities at thebeginning of cycle 2 for an agent starting
at (1,1).
// Remark : This estimation should be improved in the future
function [OUTPUT, STD]=fieldprob(WEIGHT,INPUT,RUNS, SHOW)
for i=1:RUNS,
[OUTPUT]=vmprod2(WEIGHT,INPUT),
INPUT = OUTPUT,
if (SHOW==1) then printf("RUN= %d : ",i), for j=1:length(OUTPUT), printf("
%f ",OUTPUT(1,j)),
end, printf("\n\n"),
end
end
STD=st_deviation(OUTPUT)
endfunction
//****************************************************
//Funcion to generate a m x n - Matrix with the scalar c
function[C] = matrixC(r,c,a)
C=[] //New Matrix
for i=1:c; for j=1:r; C(i,j)=a; end; end //Generate new matrix with scalar a
C
endfunction
//****************************************************
//Funcion to generate a m x n - Matrix with the scalar c out of a given matrix M
function[C] = matrixCC(M,a)
C=[] //New Matrix
[c,r]=size(M) //Get dimension of given matrix M by columns and rows
for i=1:r, for j=1:c, C(i,j)=a, end, end //Generate new matrix with scalar a
endfunction