Improving Resource Allocation Strategy Against Human Adversaries in Security Games
R Yang, C Kiekintveld, F Ordonez, M Tambe, R John
In International Joint Conference on Artificial Intelligence (IJCAI 2011).
This is the author's version of the work.
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Abstract
Recent real-world deployments of Stackelberg security
games make it critical that we address human
adversaries' bounded rationality in computing
optimal strategies. To that end, this paper
provides three key contributions: (i) new efficient
algorithms for computing optimal strategic solutions
using Prospect Theory and Quantal Response
Equilibrium; (ii) the most comprehensive experiment
to date studying the effectiveness of different
models against human subjects for security games;
and (iii) new techniques for generating representative
payoff structures for behavioral experiments in
generic classes of games. Our results with human
subjects show that our new techniques outperform
the leading contender for modeling human behavior
in security games.
