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Joint Strategy Synthesis for Multi-Agent Games of Imperfect Information against Nature
KTH, School of Electrical Engineering and Computer Science (EECS).
2021 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Gemensam strategisyntes för spel med flera agenter med ofullständig information mot naturen (Swedish)
Abstract [en]

In this paper, we investigate how agents of a multi-agent game of imperfect information may coordinate their individual strategies beforehand in order to create a better strategy for the team. The work is in the area of game theory and focuses on the previously-introduced idea of epistemic expansion. Epistemic expansion is a tool for creating simplified models of games such that strategies can be synthesised in these models. In this paper, we develop and present a new, modified definition of epistemic expansion that allows for strategies to be translated from the simplified models back to the original games. Finally, we prove that epistemic expansion satisfies the important property that any winning strategy in the simplified model can be translated into a winning strategy in the original game. 

Abstract [sv]

I den här uppsatsen undersöker vi hur agenter i ett spel med flera agenter med ofullständig information kan koordinera sina individuella strategier i förväg för att kunna skapa en bättre strategi för laget. Arbetet ligger inom området spelteori och fokuserar på den tidigare idén epistemic expansion. Epistemic expansion är ett verktyg för att skapa förenklade modeller av spel så att strategier kan bildas i dessa modeller. I den här uppsatsen utvecklar och presenterar vi en ny, modifierad definition av epistemic expansion som möjliggör att strategier kan översättas från de förenklade modellerna tillbaka till de ursprungliga spelen. Avslutningsvis bevisar vi att epistemic expansion har den viktiga egenskapen att varje vinnande strategi i en förenklad modell kan översättas till en vinnande strategi i det ursprungliga spelet.

Place, publisher, year, edition, pages
2021. , p. 21
Series
TRITA-EECS-EX ; 2021:467
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-302438OAI: oai:DiVA.org:kth-302438DiVA, id: diva2:1596767
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2021-09-24 Created: 2021-09-23 Last updated: 2022-06-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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