ICGA Journal: The Journal of the Computer Games Community

The increasing strength of chess engines and their fine-tuned ability to evaluate positions provide the opportunity to use computer analysis for assessing the strength of players on a move-by-move basis, as a game is being played. As each player makes a move on the board, a sufficiently strong engine will be able to determine whether that move has contributed to improve or degrade the position. This change in position evaluation is defined as the gain per move. In this article, we assume that it is possible to characterise a player by a distribution of gain over several moves, preferably across several games. We present an approach to estimate the strength of players based on their distributions of gain. This estimated strength is expressed in terms of a perceived Elo rating, which may differ from a player’s actual rating. However, the average of the perceived Elo ratings is equal to the average of the actual Elo ratings for a set of players. A critical factor in the approach is the need to determine the strength of the engine used for analysis. Once this is obtained, it is possible to carry out all sorts of comparisons between players, even players from different eras who never competed against each other. There are many potential applications but here we focus mainly on developing the approach, using a few recent tournaments as well as some historical events for illustrative purposes.

The following chapter from Evolutionary Game Design (Browne, 2011) describes Yavalath, the first computer-generated board game to be commercially published.

Yavalath is the most successful game evolved by the LUDI general game system. This article gives a complete description of the rules and analyses the forcing move mechanism that makes Yavalath so interesting. Some strong patterns and tactical tips are given, in addition to the solution of a puzzle and other aspects such as the first-move advantage, draws, and adding a third player. The publication of Yavalath and its reception by game players and designers is briefly discussed, as well as its general acceptance among the board game community and its possible role as an inspiration for subsequent games.

The note explains why the ko rule, which is essential for the game of Go, is not needed for hexagonal versions of the game. This known property was recently re-discovered through an evolutionary search for new games.

It was a pleasure to read the clear article on dynamic komi, written by Petr Baudis (2011) in the September issue of the ICGA Journal. Especially his first two sentences were a concise description of the basic problem: “Monte-Carlo Tree Search tends to produce unstable and unreasonable results (...) when used in positions with an extreme advantage or disadvantage. This is due to a poor move selection because of the low signal-to-noise ratio.”

In this note I want to provide additional material on different aspects of dynamic komi: (1) on the history of the name, (2) on manually operated dynamic komi, (3) on hard workers in the sewerage, and (4) on a newly created handicap prize.