Intelligent Game Play by Robots

Ingo Althofer, September 18, 2015; Update March 31, 2016

One of my research interests is intelligent game play by computers. More difficult than this is intelligent play of games with a physical component by robots. For a long time already I have been thinking about three rather different challenges.

(1) Frisbee Go, played by Robots

In 2000, the annual European Go Congress took place in Berlin-Strausberg. There were lots of events and side events. One eye catcher was a 9x9 net on a green meadow. There were lots of blue and white frisbees, and they were used as playing stones in a 9x9 go game. The net was the board.

The players had to keep some meters distance from the net and - when it was their turn - had to throw one of the frisbees of their color. If it landed on a free crossing of the net, this counted as a move. If not, there was the chance of repetition. After three unsuccessful tries the move was counted as a pass move.

To be successful in Frisbee Go, a player needs the understanding of Go, the skill to steer the frisbees, and - knowing the own frisbee skill and that of the opponent - to take this into consideration when chosing a move.

There exist already robots "shooting" frisbees. Have a look for instance in
this video.
Anyhow, the task to play Frisbee Go on an "interesting" level is far from non-trivial. Instead of handicap stones (like in traditional go) stronger players may have to throw from a larger distance.

The German Go Association has a set with a 9x9-net, 41 dark blue, and 40 white frisbee disks for frisbee go...

Frisbee Go Simulation

The Computer Olympiad 2016, organized by the ICGA, will take place between June 27 and July 03 in Leiden (NL). One of the new disciplines is "Frisbee Go Simulation" on 9x9 boards. It is a fully digital game, however with an element of chance: When a stone is meant to be placed on cell (i,j), then it will go there only with probability 1 - 4*eps. Each of the four neighboring cells, namely (i-1,j), (i+1,j), (i,j-1), (i,j+1) will be hit with probability eps each. The competition in 2016 will be played with eps= 1/8.

(2) Robot Blitz Chess on Very Big Boards

Here, by "big" I do not mean chess boards with for instance 20x20 squares, but 8x8 boards in outdoor size. Typically, on such a board each square has side length 50 centimeters. So the board in total is 4m x 4m. Blitz Chess means that play is executed under strict (and short) time limits, for instance 5 minutes for each side for the whole game. In performance-oriented outdoor chess, the chess clock is placed on a stand, in the middle at one side of the board (often near the squares a4 and a5).

The robots shall perform "all" tasks: recognizing the position, computing, moving the pieces, operating the clock.

A simplified version of this took place in May 2011, in an exhibition event in Moscow: small/normal chess board, blitz chess by robots (two different "machines" from Germany's KuKa company), simplified task of board recognition by "many" helpful marks in and around the board (total size 60cm x 60cm). The robots were fixed on stands and each one had an arm for executing the moves.

Michael Hartisch (in those days math student at Jena University) took this event as a starting point for an investigation in his B.Sc. thesis. Michael developed an algorithm to compute endgame data bases for the situation where each move has its own length (instead of the standard length "1"). For instance, in the basic endgame with King plus Rook vs. King it turned out that there are several situations where the optimal robot move is different from the optimal move in classic "distance to mate" fashion.

Robot Blitz Chess on big boards will be very entertaining, also for spectators who do not understand the intricacies of chess.

(3) Robot Table Tennis

In 2014, almost simultaneously two Youtube videos showed robots playing table tennis on a (very) high level.

Ulf Hoffmann Tischtennis Roboter
Timo Boll vs. KUKA Robot

Soon it became common knowledge that both videos only showed fake duels. Having robots mastering table tennis is a challenge still far beyond the horizon of current technology. Nevertheless, I hope to be still alive when a robot challenges the human World Champion in table tennis

Comparing the Tasks

All three tasks are real challenges. If I had to order them by increasing difficulty (for robot strength in competitive play against strong humans), my answer would be:

Robot Outdoor Blitz Chess
is easier than
Robot Frisbee Go
is easier than
Robot Table Tennis.

But who knows ...

My Background

One of my backgrounds is intelligent game play by computers: Back in 1985, I introduced the 3-Hirn approach and applied it to the game of chess. Between 1985 and 1997 many experiments showed that 3-Hirn gives 200 extra rating points for a wide range of computer strengths and opponents. The experiences are documented in many reports and in particular in the book "13 Jahre 3-Hirn - Meine Schach-Experimente mit Mensch-Maschinen-Kombinationen". The book is written in German, a rough translation of the title is "13 Years of 3-Hirn - My Chess Experiments with Man-Machine Combinations". Here "machine" is meant in its plural form.

One of my doctoral students is Stefan Meyer-Kahlen, the father of chess program Shredder. Shredder won 18 World Championship titles (in words: eighteen) in computer chess between 1996 and 2015.

Together, my diploma student Timo Klaustermeyer (now Timo Haupt) and I introduced the concept of "Freestyle Chess" back in 2004: Freestyle chess is played by teams. Each team has a distinguished captain and arbitrary helpers. These helpers may be other human players, books, magazines, computer programs, data bases, and whatever else might help. (Later, Garry Kasparov adopted this term, forgetting to mention its creators...)

Since year 2000, I am active in the field of computer-aided Go (Go is the famous traditional Asian board game) - with two hopes:

* To help beating the best human players by "freestyle teams" of amateurs and computer programs before 2020.

* To help that computer Go programs alone (without human help) beat the best human players before 2027.

Currently, the best strong go bot for analysis purposes is CrazyStone 2013, developed by pioneer Remi Coulom. See this screenshot:

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