Comparing human behavior models in repeated Stackelberg security games: an extended study
DOI10.1016/j.artint.2016.08.002zbMath1390.91079OpenAlexW2514333820MaRDI QIDQ329050
Fei Fang, Francesco M. Delle Fave, Nicole Sintov, Arnaud Lyet, Milind Tambe, Debarun Kar
Publication date: 21 October 2016
Published in: Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.artint.2016.08.002
Hierarchical games (including Stackelberg games) (91A65) Rationality and learning in game theory (91A26) Multistage and repeated games (91A20) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
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- Handling attrition in longitudinal studies: the case for refreshment samples
- Improving resource allocation strategies against human adversaries in security games: an extended study
- Robust solutions to Stackelberg games: addressing bounded rationality and limited observations in human cognition
- Separating curvature and elevation: a parametric probability weighting function
- A payoff-based learning procedure and its application to traffic games
- Probability weighting and the `level' and `spacing' of outcomes: an experimental study over losses
- Advances in prospect theory: cumulative representation of uncertainty
- On the convergence of reinforcement learning
- Commitment and observability in games
- Security and Game Theory
- Learning and Approximating the Optimal Strategy to Commit To
- Prospect Theory: An Analysis of Decision under Risk
- The Probability Weighting Function
- Reinforcement-based vs. Belief-based Learning Models in Experimental Asymmetric-information Games
- Lottery decisions and probability weighting function
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