Gradient Methods for Solving Stackelberg Games

Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally discrete and low dimensional. In AML, decisions are made by algorithms and are usually continuous and high dimensional, e.g. choosing the weights of a neural network. As closed form solutions for Stackelberg games generally do not exist, it is mandatory to have efficient algorithms to search for numerical solutions. We study two different procedures for solving this type of games using gradient methods. We study time and space scalability of both approaches and discuss in which situation it is more appropriate to use each of them. Finally, we illustrate their use in an adversarial prediction problem.Comment: Accepted in ADT Conference 201

21945-970 – Rio de Janeiro – Brasil

One of the main characteristics of a subway line is its large transport capacity (e.g., about 60000 travelers per hour in the Parisian subway) combined with a regular transport supply. The regularity is particularly important at rush time – peak hours – when an incident can provoke important delays. Experience shows that the consequences of an incident are highly dependent on the context in which the incident occurs (e.g., peak hours or not). The decisions taken by the operators are heavily relied on the incident context, and operators often make different decisions for the same incident in different contexts. The project SART (French acronym for Support system for traffic control) aims at developing an intelligent decision support system able of helping the operator in making decisions to solve an incident occurring on a line. This system relies on the notion of context. Context includes information and knowledge on the situation that do not intervene directly in the incident solving, but constrain the way in which the operator will choose a strategy at each step of the incident solving. The paper describes the SART project and highlights how Artificial Intelligence (AI) techniques can contribute to knowledge acquisition and knowledge representation associated with its context of use. Particularly we discuss the notion of context and show how we use this notion to solve a real-world problem

SART: An Intelligent Assistant System for Subway Control

One of the main characteristics of a subway line is its large transport capacity (e.g., about 60000 travelers per hour in the Parisian subway) combined with a regular transport supply. The regularity is particularly important at rush time -- peak hours -- when an incident can provoke important delays. Experience shows that the consequences of an incident are highly dependent on the context in which the incident occurs (e.g., peak hours or not). The decisions taken by the operators are heavily relied on the incident context, and operators often make different decisions for the same incident in different contexts. The project SART (French acronym for Support system for traffic control) aims at developing an intelligent decision support system able of helping the operator in making decisions to solve an incident occurring on a line. This system relies on the notion of context. Context includes information and knowledge on the situation that do not intervene directly in the incident solving, but constrain the way in which the operator will choose a strategy at each step of the incident solving. The paper describes the SART project and highlights how Artificial Intelligence (AI) techniques can contribute to knowledge acquisition and knowledge representation associated with its context of use. Particularly we discuss the notion of context and show how we use this notion to solve a real-world problem