Online Evolution for Multi-Action Adversarial Games
(2016) Evostar 2016 In Lecture Notes in Computer Science 9597. p.590-603- Abstract
- We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very... (More)
- We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very effective for this sort of problems. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8569733
- author
- Justesen, Niels ; Mahlmann, Tobias LU and Togelius, Julian
- organization
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- evolutionary computation, monte carlo tree search, games, online evolution
- host publication
- Applications of Evolutionary Computation 2016
- series title
- Lecture Notes in Computer Science
- editor
- Burelli, Paolo and Squillero, Giovanni
- volume
- 9597
- pages
- 13 pages
- publisher
- Springer
- conference name
- Evostar 2016
- conference dates
- 2016-03-30
- external identifiers
-
- scopus:84961750938
- ISSN
- 0302-9743
- ISBN
- 978-3-319-31204-0
- 978-3-319-31203-3
- DOI
- 10.1007/978-3-319-31204-0_38
- language
- English
- LU publication?
- yes
- id
- 6e11bd69-842b-4d23-a9d1-92d3af7dd18a (old id 8569733)
- date added to LUP
- 2016-04-04 10:45:01
- date last changed
- 2024-02-28 16:57:06
@inproceedings{6e11bd69-842b-4d23-a9d1-92d3af7dd18a,
abstract = {{We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very effective for this sort of problems.}},
author = {{Justesen, Niels and Mahlmann, Tobias and Togelius, Julian}},
booktitle = {{Applications of Evolutionary Computation 2016}},
editor = {{Burelli, Paolo and Squillero, Giovanni}},
isbn = {{978-3-319-31204-0}},
issn = {{0302-9743}},
keywords = {{evolutionary computation; monte carlo tree search; games; online evolution}},
language = {{eng}},
pages = {{590--603}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science}},
title = {{Online Evolution for Multi-Action Adversarial Games}},
url = {{https://lup.lub.lu.se/search/files/5613107/8569741.pdf}},
doi = {{10.1007/978-3-319-31204-0_38}},
volume = {{9597}},
year = {{2016}},
}