Abstract
Effective search control is one of the key components of any successful simulation-based game-playing program. In General Game Playing (GGP), learning of useful search-control knowledge is a particularly challenging task because it must be done in real-time during online play. In here we describe the search-control techniques used in the 2010 version of the GGP agent CadiaPlayer, and show how they have evolved over the years to become increasingly effective and robust across a wide range of games. In particular, we present a new combined search-control scheme (RAVE/MAST/FAST) for biasing action selection. The scheme proves quite effective on a wide range of games including chess-like games, which have up until now proved quite challenging for simulation-based GGP agents.
| Original language | English |
|---|---|
| Pages (from-to) | 9-16 |
| Number of pages | 8 |
| Journal | KI - Kunstliche Intelligenz |
| Volume | 25 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Mar 2011 |
Bibliographical note
Publisher Copyright: © 2010, Springer-Verlag.Other keywords
- Cadiaplayer
- General game playing
- Monte Carlo Tree Search
- Search control
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