71,356 research outputs found
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
The Nash Threats Folk Theorem With Communication and Approximate Common Knowledge In Two Player Games
Abstract Not Available At This Time
Self-Serving Biases in Bargaining
There is strong evidence that in bargaining situations with asymmetric outside options people exhibit self-serving biases concerning their fairness judgements. Moreover, psychological literature suggests that this can be a driving force of bargaining impasse. This paper extends the notion of inequity aversion to incorporate self-serving biases due to asymmetric outside options and analyses whether this leads to bargaining breakdown. I distinguish between sophisticated and naive agents, that is, those agents who understand their bias and those who do not. I find that breakdown in ultimatum bargaining results from naiveté of the proposers
Solving Large Extensive-Form Games with Strategy Constraints
Extensive-form games are a common model for multiagent interactions with
imperfect information. In two-player zero-sum games, the typical solution
concept is a Nash equilibrium over the unconstrained strategy set for each
player. In many situations, however, we would like to constrain the set of
possible strategies. For example, constraints are a natural way to model
limited resources, risk mitigation, safety, consistency with past observations
of behavior, or other secondary objectives for an agent. In small games,
optimal strategies under linear constraints can be found by solving a linear
program; however, state-of-the-art algorithms for solving large games cannot
handle general constraints. In this work we introduce a generalized form of
Counterfactual Regret Minimization that provably finds optimal strategies under
any feasible set of convex constraints. We demonstrate the effectiveness of our
algorithm for finding strategies that mitigate risk in security games, and for
opponent modeling in poker games when given only partial observations of
private information.Comment: Appeared in AAAI 201
Approximating n-player behavioural strategy nash equilibria using coevolution
Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the minimax solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash. Copyright 2011 ACM
No-Regret Learning in Extensive-Form Games with Imperfect Recall
Counterfactual Regret Minimization (CFR) is an efficient no-regret learning
algorithm for decision problems modeled as extensive games. CFR's regret bounds
depend on the requirement of perfect recall: players always remember
information that was revealed to them and the order in which it was revealed.
In games without perfect recall, however, CFR's guarantees do not apply. In
this paper, we present the first regret bound for CFR when applied to a general
class of games with imperfect recall. In addition, we show that CFR applied to
any abstraction belonging to our general class results in a regret bound not
just for the abstract game, but for the full game as well. We verify our theory
and show how imperfect recall can be used to trade a small increase in regret
for a significant reduction in memory in three domains: die-roll poker, phantom
tic-tac-toe, and Bluff.Comment: 21 pages, 4 figures, expanded version of article to appear in
Proceedings of the Twenty-Ninth International Conference on Machine Learnin
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