Detail publikace

Creating Action Heuristics for General Game Playing Agents

TRUTMAN, M. SCHIFFEL, S.

Originální název

Creating Action Heuristics for General Game Playing Agents

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Monte-Carlo Tree Search (MCTS) is the most popular search algorithm used in General Game Playing (GGP) nowadays mainly because of its ability to perform well in the absence of domain knowledge. Several approaches have been proposed to add heuristics to MCTS in order to guide the simulations. In GGP those approaches typically learn heuristics at runtime from the results of the simulations. Because of peculiarities of GGP, it is preferable that these heuristics evaluate actions rather than game positions. We propose an approach that generates heuristics that estimate the usefulness of actions by analyzing the game rules as opposed to the simulation results. We present results of experiments that show the potential of our approach.

Klíčová slova

Goal Condition, Heuristic Function, Game State, Game Tree, General Game

Autoři

TRUTMAN, M.; SCHIFFEL, S.

Vydáno

31. 12. 2016

Nakladatel

Springer Verlag

Místo

Berlín

ISSN

1865-0929

Periodikum

Communications in Computer and Information Science

Ročník

614

Číslo

1

Stát

Spolková republika Německo

Strany od

149

Strany do

164

Strany počet

16

URL

https://link.springer.com/chapter/10.1007/978-3-319-39402-2_11

BibTex

@inproceedings{BUT163390,
  author="TRUTMAN, M. and SCHIFFEL, S.",
  title="Creating Action Heuristics for General Game Playing Agents",
  booktitle="Computer Games, CGW 2015",
  year="2016",
  series="Communications in Computer and Information Science",
  journal="Communications in Computer and Information Science",
  volume="614",
  number="1",
  pages="149--164",
  publisher="Springer Verlag",
  address="Berlín",
  doi="10.1007/978-3-319-39402-2\{_}11",
  issn="1865-0929",
  url="https://link.springer.com/chapter/10.1007/978-3-319-39402-2_11"
}