166 research outputs found
A Stackelberg Strategy for Routing Flow over Time
Routing games are used to to understand the impact of individual users'
decisions on network efficiency. Most prior work on routing games uses a
simplified model of network flow where all flow exists simultaneously, and
users care about either their maximum delay or their total delay. Both of these
measures are surrogates for measuring how long it takes to get all of a user's
traffic through the network. We attempt a more direct study of how competition
affects network efficiency by examining routing games in a flow over time
model. We give an efficiently computable Stackelberg strategy for this model
and show that the competitive equilibrium under this strategy is no worse than
a small constant times the optimal, for two natural measures of optimality
Bridging the user equilibrium and the system optimum in static traffic assignment: a review
Solving the road congestion problem is one of the most pressing issues in modern cities since it causes time wasting, pollution, higher industrial costs and huge road maintenance costs. Advances in ITS technologies and the advent of autonomous vehicles are changing mobility dramatically. They enable the implementation of a coordination mechanism, called coordinated traffic assignment, among the sat-nav devices aiming at assigning paths to drivers to eliminate congestion and to reduce the total travel time in traffic networks. Among possible congestion avoidance methods, coordinated traffic assignment is a valuable choice since it does not involve huge investments to expand the road network. Traffic assignments are traditionally devoted to two main perspectives on which the well-known Wardropian principles are inspired: the user equilibrium and the system optimum. User equilibrium is a user-driven traffic assignment in which each user chooses the most convenient path selfishly. It guarantees that fairness among users is respected since, when the equilibrium is reached, all users sharing the same origin and destination will experience the same travel time. The main drawback in a user equilibrium is that the system total travel time is not minimized and, hence, the so-called Price of Anarchy is paid. On the other hand, the system optimum is an efficient system-wide traffic assignment in which drivers are routed on the network in such a way the total travel time is minimized, but users might experience travel times that are higher than the other users travelling from the same origin to the same destination, affecting the compliance. Thus, drawbacks in implementing one of the two assignments can be overcome by hybridizing the two approaches, aiming at bridging usersâ fairness to system-wide efficiency. In the last decades, a significant number of attempts have been done to bridge fairness among users and system efficiency in traffic assignments. The survey reviews the state-of-the-art of these trade-off approaches
Using Collective Intelligence to Route Internet Traffic
A COllective INtelligence (COIN) is a set of interacting reinforcement
learning (RL) algorithms designed in an automated fashion so that their
collective behavior optimizes a global utility function. We summarize the
theory of COINs, then present experiments using that theory to design COINs to
control internet traffic routing. These experiments indicate that COINs
outperform all previously investigated RL-based, shortest path routing
algorithms.Comment: 7 page
Designing the Game to Play: Optimizing Payoff Structure in Security Games
Effective game-theoretic modeling of defender-attacker behavior is becoming
increasingly important. In many domains, the defender functions not only as a
player but also the designer of the game's payoff structure. We study
Stackelberg Security Games where the defender, in addition to allocating
defensive resources to protect targets from the attacker, can strategically
manipulate the attacker's payoff under budget constraints in weighted L^p-norm
form regarding the amount of change. Focusing on problems with weighted
L^1-norm form constraint, we present (i) a mixed integer linear program-based
algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm
with improved efficiency achieved by effective pruning; (iii) a polynomial time
approximation scheme for a special but practical class of problems. In
addition, we show that problems under budget constraints in L^0-norm form and
weighted L^\infty-norm form can be solved in polynomial time. We provide an
extensive experimental evaluation of our proposed algorithms
An Approximation Algorithm for Stackelberg Network Pricing
We consider the problem of maximizing the revenue raised from tolls set on
the arcs of a transportation network, under the constraint that users are
assigned to toll-compatible shortest paths. We first prove that this problem is
strongly NP-hard. We then provide a polynomial time algorithm with a worst-case
precision guarantee of , where denotes the number of
toll arcs. Finally we show that the approximation is tight with respect to a
natural relaxation by constructing a family of instances for which the
relaxation gap is reached.Comment: 38 page
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