1 1 1 2 2 2 3 3 3

libyantawdryΤεχνίτη Νοημοσύνη και Ρομποτική

23 Οκτ 2013 (πριν από 4 χρόνια και 16 μέρες)

73 εμφανίσεις


M. Gams

Jozef Stefan
Institute



PURPOSE:




Show how AI methods work

-

to con
-
computer specialists

-

with a simple, understandable
example

THE EXAMPLE:


Solution:
1 2 3


Possibilities


1 1 1


2 2 2


3 3 3



3 x 3 x 3 = 27 possibilities




100


3 … no. of all particles in the universe

GENETIC ALGORITHMS


Population evolution: breeding, selection;

competition, better adapted offsprings


only the
best survive



Genetic code of an individual is represented by a
sequence of digits, 3 seeds: 111, 222, 321


Cross
-
over (breeding)


111 + 222


122(2), 212(0), 221(1), 211(0), 121(2), 112(1)


-

()
123


Mutation (each third individual, next position, +1)

2
22(1), 212(0), 221(1), 2
2
1(1), 121(2), 112(1)


GENETIC ALGORITHMS


111, 222, 321


1+2
2
22(1), 212(0), 221(1), 2
2
1(1), 121(2), 112(1)

1+3 12
2
(2), 311(0), 321(1),
1
11(1), 121(2), 111(1)

2+3 2
3
1(0), 321(1), 322(1), 32
3
(2), 222(1), 221(1)


3 BEST: 121(2), 122(2), 323(2)


NEXT STEP: SEVERAL SOLUTIONS 123(3)

RULES:


if high_fever then illness

if fever > 37 then illness



if (axilliar = yes) and (degree of diff = fairly) and (lung =
no) and (sex = female) then breast (100%)



Solution:


if
x1=1 and x2=2 in x3=3 then (3).



RULES:


Start: 111

2 candidates


First step:

if x1=1 then (1)

if x1=2 then (0)

if x1=3 then (0)


Next step:

if x1=1 and x2=2 then (2)

if x1=2 and x2=2 then (1)


Next step:

if x1=1 and x2=2 and x3=3 then solution (3)


TREES:

Idea


repeat splitting
the space of all
possibilities

11(1)

12(2)

13(1)

121(2)

122(2)

123(3)

x1=1

2x2x3

3x2x3

1x2x3

NE

DA

x1=1

NE


x2=2

x3=3


NE

NE


123


NE

DA

NE

DA

NE

DA

x1=1

11x3

13x3


x2=2

12x3

NE

NE

DA

NE

DA

NEURAL NETWORKS:


Output(neuron) = 1 if
Σ

w
i

x
i

> C


0

otherwise


Our case: 9 connections

if w
1

= 1, w
5

= 1 w
9

= 1,

Σ

w
i

x
i

= 3

100010001

1
231
2
312
3


x1 x2 x3

NEURAL NETWORKS:






Learn

weights
w
i

examples

in a sequence


3 3 3 (1) 111111111 (3)


3 3 2 (0) 11
0
11
0
111 (1)


3 3 1 (0) 1101101
0
1 (1)


3 2 3 (2) 110110
0
01 (2)


3 2 2 (1) 110110001 (1)


3 2 1 (1) 110110001 (1)


3 1 3 (1) 110110001 (2)


3 1 2 (0) 110010001 (0)


3 1 1 (0) 110010001 (0)


2 3 3 (1) 110010001 (2)


1
0
0010001




CONCLUSION






-

DIFFERENT AI SEARCH METHODS


GENETIC/EVOLUTIONARY METHODS


RULE
-
CONSTRUCTING MACHINE LEARNING


TREE
-
CONSTRUCTING


NEURAL NETWORKS



-

AROUND 10 ADDITIONAL METHODS


SOME DETERMINISTIC, OTHER SOFT



-

DIFFERENT METHODS APPROPRIATE


FOR DIFFERENT PROBLEMS AND TASKS


-

EFFECTIVE, MANY TOOLS


http://ai.ijs.si/