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Games 2018, 9(3), 39;

An Abstraction-Refinement Methodologyfor Reasoning about Network Games

The Institute of Science and Technology Austria (IST Austria), Am Campus 1, 3400 Klosterneuburg, Austria
Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Bruxelles, Belgium
School of Computer Science and Engineering, Hebrew University, Jerusalem 91904, Israel
This paper is an extended version of our paper published in Proceedings of the 26th International Joint Conference on Artificial Intelligence 2017 (IJCAI’17), Melbourne, Australia, 19–25 August 2017.
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 2 May 2018 / Revised: 14 June 2018 / Accepted: 17 June 2018 / Published: 22 June 2018
(This article belongs to the Special Issue Logic and Game Theory)
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Network games (NGs) are played on directed graphs and are extensively used in network design and analysis. Search problems for NGs include finding special strategy profiles such as a Nash equilibrium and a globally-optimal solution. The networks modeled by NGs may be huge. In formal verification, abstraction has proven to be an extremely effective technique for reasoning about systems with big and even infinite state spaces. We describe an abstraction-refinement methodology for reasoning about NGs. Our methodology is based on an abstraction function that maps the state space of an NG to a much smaller state space. We search for a global optimum and a Nash equilibrium by reasoning on an under- and an over-approximation defined on top of this smaller state space. When the approximations are too coarse to find such profiles, we refine the abstraction function. We extend the abstraction-refinement methodology to labeled networks, where the objectives of the players are regular languages. Our experimental results demonstrate the effectiveness of the methodology. View Full-Text
Keywords: network formation games; abstraction-refinement; Nash equilibrium; social optimum network formation games; abstraction-refinement; Nash equilibrium; social optimum

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Avni, G.; Guha, S.; Kupferman, O. An Abstraction-Refinement Methodologyfor Reasoning about Network Games. Games 2018, 9, 39.

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