Regret-based optimization and preference elicitation for stackelberg security games with uncertainty

Thanh H. Nguyen, Amulya Yadav, Bo An, Milind Tambe, Craig Boutilier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Stackelberg security games (SSGs) have been deployed in a number of real-world domains. One key challenge in these applications is the assessment of attacker payoffs, which may not be perfectly known. Previous work has studied SSGs with uncertain payoffs modeled by interval uncertainty and provided maximin-based robust solutions. In contrast, in this work we propose the use of the less conservative minimax regret decision criterion for such payoff-uncertain SSGs and present the first algorithms for computing minimax regret for SSGs. We also address the challenge of preference elicitation, using minimax regret to develop the first elicitation strategies for SSGs. Experimental results validate the effectiveness of our approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
PublisherAI Access Foundation
Pages756-762
Number of pages7
ISBN (Electronic)9781577356776
Publication statusPublished - Jan 1 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014Jul 31 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period7/27/147/31/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Nguyen, T. H., Yadav, A., An, B., Tambe, M., & Boutilier, C. (2014). Regret-based optimization and preference elicitation for stackelberg security games with uncertainty. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence (pp. 756-762). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1). AI Access Foundation.