# 1 1 1 2 2 2 3 3 3

Τεχνίτη Νοημοσύνη και Ρομποτική

23 Οκτ 2013 (πριν από 4 χρόνια και 8 μήνες)

85 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/