Navigating the Landscape of Games. Article Swipe
Shayegan Omidshafiei
,
Karl Tuyls
,
Wojciech Marian Czarnecki
,
Francisco C. Santos
,
Mark Rowland
,
Jerome T. Connor
,
Daniel Hennes
,
Paul Müller
,
Julien Pérolat
,
Bart De Vylder
,
Audrūnas Gruslys
,
Rémi Munos
·
YOU?
·
· 2020
· Open Access
·
YOU?
·
· 2020
· Open Access
·
Games are traditionally recognized as one of the key testbeds underlying progress in artificial intelligence (AI), aptly referred to as the Drosophila of AI. Traditionally, researchers have focused on using games to build strong AI agents that, e.g., achieve human-level performance. This progress, however, also requires a classification of how 'interesting' a game is for an artificial agent. Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but also helps to determine what game an AI should address next as part of its training. Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes, characteristics, and complexities. We illustrate our findings in various domains, ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another. Our results culminate in a demonstration of how one can leverage this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/abs/2005.01642v1
- OA Status
- green
- Cited By
- 4
- References
- 69
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3021897783
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3021897783Canonical identifier for this work in OpenAlex
- Title
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Navigating the Landscape of Games.Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-05-04Full publication date if available
- Authors
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Shayegan Omidshafiei, Karl Tuyls, Wojciech Marian Czarnecki, Francisco C. Santos, Mark Rowland, Jerome T. Connor, Daniel Hennes, Paul Müller, Julien Pérolat, Bart De Vylder, Audrūnas Gruslys, Rémi MunosList of authors in order
- Landing page
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https://arxiv.org/abs/2005.01642v1Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/abs/2005.01642v1Direct OA link when available
- Concepts
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Leverage (statistics), Computer science, Artificial intelligence, Game mechanics, Key (lock), Combinatorial game theory, Data science, Game theory, Machine learning, Sequential game, Mathematical economics, Mathematics, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2023: 2, 2021: 2Per-year citation counts (last 5 years)
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69Number of works referenced by this work
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| abstract_inverted_index.can | 166 |
| abstract_inverted_index.for | 54 |
| abstract_inverted_index.how | 49, 96, 164 |
| abstract_inverted_index.its | 91 |
| abstract_inverted_index.key | 8 |
| abstract_inverted_index.new | 173 |
| abstract_inverted_index.not | 62 |
| abstract_inverted_index.one | 5, 155, 165 |
| abstract_inverted_index.our | 130 |
| abstract_inverted_index.the | 7, 20, 68, 108, 117, 147 |
| abstract_inverted_index.This | 41 |
| abstract_inverted_index.also | 44, 77 |
| abstract_inverted_index.from | 136, 183 |
| abstract_inverted_index.game | 52, 82 |
| abstract_inverted_index.have | 26 |
| abstract_inverted_index.more | 142 |
| abstract_inverted_index.next | 87 |
| abstract_inverted_index.only | 63 |
| abstract_inverted_index.part | 89 |
| abstract_inverted_index.real | 184 |
| abstract_inverted_index.show | 95 |
| abstract_inverted_index.this | 59, 168 |
| abstract_inverted_index.what | 81 |
| abstract_inverted_index.(AI), | 15 |
| abstract_inverted_index.Games | 0 |
| abstract_inverted_index.Here, | 93 |
| abstract_inverted_index.aptly | 16 |
| abstract_inverted_index.build | 32 |
| abstract_inverted_index.e.g., | 37 |
| abstract_inverted_index.games | 30, 106, 120, 139, 145, 181 |
| abstract_inverted_index.helps | 78 |
| abstract_inverted_index.that, | 36 |
| abstract_inverted_index.using | 29 |
| abstract_inverted_index.world | 185 |
| abstract_inverted_index.agent. | 57 |
| abstract_inverted_index.agents | 35, 73, 152 |
| abstract_inverted_index.enable | 107 |
| abstract_inverted_index.games, | 75, 115, 176 |
| abstract_inverted_index.games. | 186 |
| abstract_inverted_index.graphs | 103 |
| abstract_inverted_index.latter | 60 |
| abstract_inverted_index.learnt | 71 |
| abstract_inverted_index.pitted | 153 |
| abstract_inverted_index.should | 85 |
| abstract_inverted_index.sizes, | 124 |
| abstract_inverted_index.strong | 33 |
| abstract_inverted_index.useful | 112 |
| abstract_inverted_index.widely | 122 |
| abstract_inverted_index.achieve | 38 |
| abstract_inverted_index.address | 86 |
| abstract_inverted_index.against | 154 |
| abstract_inverted_index.applied | 99 |
| abstract_inverted_index.between | 119 |
| abstract_inverted_index.complex | 143 |
| abstract_inverted_index.focused | 27 |
| abstract_inverted_index.network | 97 |
| abstract_inverted_index.ranging | 135 |
| abstract_inverted_index.results | 158 |
| abstract_inverted_index.trained | 150 |
| abstract_inverted_index.various | 133 |
| abstract_inverted_index.varying | 123 |
| abstract_inverted_index.Tackling | 58 |
| abstract_inverted_index.another. | 156 |
| abstract_inverted_index.creation | 109 |
| abstract_inverted_index.domains, | 134 |
| abstract_inverted_index.findings | 131 |
| abstract_inverted_index.generate | 172 |
| abstract_inverted_index.however, | 43 |
| abstract_inverted_index.leverage | 167 |
| abstract_inverted_index.measures | 98 |
| abstract_inverted_index.mixtures | 178 |
| abstract_inverted_index.progress | 11 |
| abstract_inverted_index.question | 61 |
| abstract_inverted_index.referred | 17 |
| abstract_inverted_index.requires | 45 |
| abstract_inverted_index.response | 102 |
| abstract_inverted_index.testbeds | 9 |
| abstract_inverted_index.canonical | 138 |
| abstract_inverted_index.capturing | 146 |
| abstract_inverted_index.culminate | 159 |
| abstract_inverted_index.determine | 80 |
| abstract_inverted_index.empirical | 144, 180 |
| abstract_inverted_index.including | 177 |
| abstract_inverted_index.landscape | 113 |
| abstract_inverted_index.progress, | 42 |
| abstract_inverted_index.so-called | 101 |
| abstract_inverted_index.training. | 92 |
| abstract_inverted_index.Drosophila | 21 |
| abstract_inverted_index.artificial | 13, 56 |
| abstract_inverted_index.illustrate | 129 |
| abstract_inverted_index.recognized | 3 |
| abstract_inverted_index.underlying | 10 |
| abstract_inverted_index.facilitates | 64 |
| abstract_inverted_index.human-level | 39 |
| abstract_inverted_index.information | 169 |
| abstract_inverted_index.interesting | 175 |
| abstract_inverted_index.large-scale | 105 |
| abstract_inverted_index.performance | 148 |
| abstract_inverted_index.quantifying | 116 |
| abstract_inverted_index.researchers | 25 |
| abstract_inverted_index.synthesized | 182 |
| abstract_inverted_index.intelligence | 14 |
| abstract_inverted_index.performance. | 40 |
| abstract_inverted_index.well-studied | 137 |
| abstract_inverted_index.'interesting' | 50 |
| abstract_inverted_index.automatically | 171 |
| abstract_inverted_index.complexities. | 127 |
| abstract_inverted_index.demonstration | 162 |
| abstract_inverted_index.relationships | 118 |
| abstract_inverted_index.significantly | 141 |
| abstract_inverted_index.traditionally | 2 |
| abstract_inverted_index.understanding | 66 |
| abstract_inverted_index.Traditionally, | 24 |
| abstract_inverted_index.classification | 47 |
| abstract_inverted_index.characteristics | 69 |
| abstract_inverted_index.characteristics, | 125 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.72685602 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |