New RTS AI Competition

For those who are interested, this year's AIIDE RTS competition is using StarCraft with hooks for the AI - see http://eis.ucsc.edu/StarCraftAICompetition

The power of a million monkeys

When a million monkeys play Go, they win.

Enter the Monte Carlo method, named by its Manhattan Project pioneers for the casinos where they gambled. It consists of random simulations repeated again and again until patterns and probabilities emerge: the characteristics of an atomic bomb explosion, phase states in quantum fields, the outcome of a Go game. Programs like MoGO and Many Faces simulate random games from start to finish, over and over and over again, with no concern for figuring out which of any given move is best.

--Humans No Match for Go Bot Overlords [wired.com]

ICML 2007

The papers for ICML 2007 are now available online. As a game AI researcher, interesting papers include:

1. Learning to Solve Game Trees. David Stern, Ralf Herbrich and Thore Graepel.

Formulating a probabilistic model for nodes in a game-tree and performing best-first AND/OR search. Interesting trend in integrating statistical machine learning techniques into game-tree search techniques. See also authors' prior paper on Bayesian pattern matching in computer Go.

2. Combining Online and Offline Knowledge in UCT. Sylvain Gelly and David Silver.

UCT - Upper confidence Tree search is a promising game-tree search technique for games with high branching factor. This paper sheds some light on what does and does not work when using UCT.