Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties

How do we build multiagent algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human interaction, such as oligopolistic markets and security domains. In Stackelberg games, one player, the leader, commits to a strategy and the follower makes their decision with knowledge of the leader’s commitment. Existing algorithms for Stackelberg games efficiently find optimal solutions (leader strategy), but they critically assume that the follower plays optimally. Unfortunately, in real-world applications, agents face human followers who — because of their bounded rationality and limited observation of the leader strategy — may deviate from their expected optimal response. Not taking into account these likely deviations when dealing with human adversaries can cause an unacceptable degradation in the leader’s reward, particularly in security applications where these algorithms have seen real-world dep...

• Type: Conference
• Year: 2009
• Where: ATAL
• Authors: James Pita, Manish Jain, Fernando Ordóñez, Milind Tambe, Sarit Kraus, Reuma Magori-Cohen
• Keywords: Artificial Intelligence, ATAL 2009, Human, Human Followers, Stackelberg Games