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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" }