Games as a Test Bed for Developing AI Applications (in Physics) Brains vs Computers Symposium of the “van der Waals” study association December 10, 2013.

Games as a Test Bed for Developing AI Applications (in Physics)

Brains vs Computers

Symposium of the “van der Waals” study association

December 10, 2013

Jos UiterwijkDepartment of Knowledge Engineering

Maastricht University1/36

• Computer chess and computer games

• The role of computer games in Artificial Intelligence

• Brute force?

• The impact of knowledge and heuristics

• New developments

• AI and Physics

• Conclusions

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Overview

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Some history

• Start of computer chess

• The Turk

• It was all fake!

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Origin of the AI• Starts around 1950

• Chess as the drosophila melanogaster of AI

• 2 pioneers:– Alan Turing

– Claude Shannon

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1. Rules are simple, but the strategy is complex

2. Domain is fixed, by which programs are easily comparable, both with other programs and with humans. By the nature of a game it is easy to test if a new technique is “better”.

3. Games are typical for human intelligence (Goethe: chess is the touchstone of the intellect). This explains the interest from psychology.

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Why is chess of interest for AI?

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Alan Turing (1912-1954)

• Worked in Bletchley Park during World War II

• Decoding the German Enigma codes: The Bombe

• Was the first who seriously posed the question: Can machines think?

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Alan Turing: Turing test

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• Used the computer in his “spare time” for chess programming.

• Was the first who wrote a chess program

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Alan Turing

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Claude Shannon (1916-2001)• Was also concerned with

computer chess

• Built chess endgame machines

• Wrote the “bible” of chess programming: Programming a computer for playing chess (1950)

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Claude Shannon• Shannon was

brilliant in many domains, both theoretically and practically: he built among others a juggling robot

• By the way, he also could juggle himself quite good!

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State of the Art in computer chess

• Nowaday computers are stronger than human world champions

• Mile stone: Kasparov losing from chess machine Deep Blue in 1997

• Kramnik loses from the “simple” desk top program Deep Fritz in 2006

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Adriaan de Groot (1914-2006)

• Professor in psychology

• Studies on “chess thinking”

• PhD thesis (1946) Het Denken van den Schaker, translated (1965) as Thought and Choice in Chess

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• For many board games just used brute computer power was initially used (i.e., as many calculations as possible): dumb but fast. This is called the brute-force approach

• Later the question arose: can the brain still beat the machine by clever use of knowledge? The knowledge-based approach

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Brute force

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• Facts

• Heuristics(rules of thumb)

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Knowledge

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• The “mutilated chess board” problems:Can I put domino stones (of 2 x 1 size) in such a way on the board that all squares are covered?

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Facts (1)

??

The 4x4 problem

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The 8x8 problem

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Facts (2)

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The 20x20 problem

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Facts (3)

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• The knight jump puzzle:Can you find a route on the chess board starting at a

given location such that all squares are traveled exactly once?

• Heuristic (rule of thumb):First visit the corners, then the edges, etc., gradually

going to the centre

• Does this heuristic work?

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Heuristics (1)

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• The knight jump puzzle on the 8x8 board

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Heuristics (2)

45 32 11 16 43 34 14

10 17 44 33 12 15 42 35

31 46 59 56 61 52 13 2

18 9 62 53 58 55 36 41

47 30 57 60 51 64 3 24

8 19 50 63 54 25 40 37

29 48 21 6 27 38 23 4

20 7 28 49 22 5 26 39

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• However: a heuristic is fallible:

• The 5x5 knight jump puzzle

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Heuristics (3)

20 11 6 18

5 16 19 12 7

10 21 ? 17 2

15 4 23 8 13

22 9 14 3 24

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• Another knight jump problem

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Chosing the right representation

What is the shortest route to switch the white and black knights?

start goal

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• Step 1: number the squares:

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Solution

108 95 6 7

1 2 3 4

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• Step 2: draw the neighbour diagram for knight jumps (which squares are reachable in one jump?)

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10 8 9 5 6 7

1 2 3 4

310 6 1 8 7 2 9 4 5

from:

we get:

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• Step 3:

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B W W B W B B W

and the goal situation

Draw the start situation

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• Step 4:

Recognise that this is just a

railcar switching problem!

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• Step 5: solution now is simple:

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B W W B B

W W B B B W W

W B B W

B B W W

12 steps

14 steps

6 steps

8 steps=====

40 steps

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• Many other games have been or are target of AI research.

• Many have been solved, which means that the computer has an optimal strategy against any resistance.

• Others are played above human level

• Some are still difficult

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State of the art for other computer games

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• Until recently, strong humans refused to play against computers

• Reason: computers are too strong• (recently the human world champion nevertheless

played a match; he lost 8-0!)

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Reversi

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• Standard boards are completely solved• The Checkers program CHINOOK even gained the

official World Champion title

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Connect-Four, Domineering, Checkers

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• Until recently, strong humans refused to play against computers.

• Reason: humans are too strong!

• This is, since 10 years, rapidly changing though (Monte Carlo simulations work great!)

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Go

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• Much progress on:– Games with chance (Poker, Backgammon)– Multi-player games (Chinese checkers)– Imperfect-information games (Bridge)– Real-time strategy (RTS) games

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And many other games ...

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• Just mentioning some physics domain:– Nuclear physics (safety!) – Medical physics (data analysis; data mining)– Robotics (large progress)– Vision (still difficult)– Intelligent design– Many kinds of simulations, from microscopic

small to astronomic large

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Applications in Physics

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• They all benefit from computer science and AI in particular, such as:– Fast calculations– Machine learning– Pattern recognition / data mining

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• Chess (and other games) – Drosophila melanogaster of AI– Tool for development of new techniques– Insight into human intelligence

• Computers can play many (board) games at (supra) expert level

• Other games are still a challenge (Go).

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Conclusions

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• Can and will computers beat the human brain?– Yes, in many complex (but otherwise dumb)

domains– No, in several not so complex, but intelligent,

domains, for a long time to go!– Much game research has to be done!

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The End!

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