Abstract
We study Stackelberg security game (SSG) with multiple defenders, where heterogeneous defenders need to allocate security resources to protect a set of targets against a strategic attacker. In such games, coordination and cooperation between the defenders can increase their ability to protect their assets, but the heterogeneous preferences of the self-interested defenders often make such cooperation very difficult. In this paper, we approach the problem from the perspective of cooperative game theory and study coalition formation among the defenders. Our main contribution is a number of algorithmic results for the computation problems that arise in this model. We provide a poly-time algorithm for computing a solution in the core of the game and show that all of the elements in the core are Pareto efficient. We show that the problem of computing the entire core is NP-hard and then delve into a special setting where the size of a coalition is limited up to some threshold. We analyse the parameterized complexity of deciding if a coalition structure is in the core under this special setting, and provide a poly-time algorithm for computing successful deviation strategies for a given coalition.
Original language | English |
---|---|
Title of host publication | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 5603-5610 |
Number of pages | 8 |
ISBN (Electronic) | 9781713835974 |
State | Published - 2021 |
Event | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online Duration: 2 Feb 2021 → 9 Feb 2021 |
Publication series
Name | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
---|---|
Volume | 6B |
Conference
Conference | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
---|---|
City | Virtual, Online |
Period | 2/02/21 → 9/02/21 |
Bibliographical note
Funding Information:This work has been supported in part by the Israel Science Foundation under grant 1958/20 and the EU project TAILOR under Grant 992215. Jiarui Gan was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 945719).
Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.