scholarly journals Stochastic Games with Disjunctions of Multiple Objectives

2021 ◽  
Vol 346 ◽  
pp. 83-100
Author(s):  
Tobias Winkler ◽  
Maximilian Weininger
Keyword(s):  
Stochastic Games ◽  
Multiple Objectives

scholarly journals Compositional strategy synthesis for stochastic games with multiple objectives

2018 ◽  
Vol 261 ◽  
pp. 536-587 ◽  
Author(s):  
N. Basset ◽  
M. Kwiatkowska ◽  
C. Wiltsche
Keyword(s):  
Stochastic Games ◽  
Multiple Objectives

scholarly journals On Stochastic Games with Multiple Objectives

2013 ◽  
pp. 266-277 ◽  
Author(s):  
Taolue Chen ◽  
Vojtěch Forejt ◽  
Marta Kwiatkowska ◽  
Aistis Simaitis ◽  
Clemens Wiltsche
Keyword(s):  
Stochastic Games ◽  
Multiple Objectives

A Warranty Cost Estimation and Production Problem Using Multiple Objectives

1989 ◽  
Vol 8 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Jayprakash G. Patankar ◽  
Amitava Mitra
Keyword(s):  
Cost Estimation ◽  
Multiple Objectives ◽  
Production Problem ◽  
Warranty Cost

Random Walk Analysis in Antagonistic Stochastic Games

2010 ◽  
Author(s):  
Jewgeni H. Dshalalow
Keyword(s):  
Random Walk ◽  
Stochastic Games ◽  
Random Walk Analysis

Implicit Collusion in Discounted Stochastic Games with Private State Observations

2001 ◽  
Author(s):  
Andreas Krause
Keyword(s):  
Stochastic Games

scholarly journals Cooperative Stochastic Games with Mean-Variance Preferences

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 230
Author(s):  
Elena Parilina ◽  
Stepan Akimochkin
Keyword(s):  
Standard Deviation ◽  
Stochastic Games ◽  
Stochastic Game ◽  
Stochastic Variable ◽  
Expected Payoff ◽  
The Core ◽  
Risk Sensitive ◽  
Payoff Functions ◽  
Cooperative Solutions ◽  
Mean Variance

In stochastic games, the player’s payoff is a stochastic variable. In most papers, expected payoff is considered as a payoff, which means the risk neutrality of the players. However, there may exist risk-sensitive players who would take into account “risk” measuring their stochastic payoffs. In the paper, we propose a model of stochastic games with mean-variance payoff functions, which is the sum of expectation and standard deviation multiplied by a coefficient characterizing a player’s attention to risk. We construct a cooperative version of a stochastic game with mean-variance preferences by defining characteristic function using a maxmin approach. The imputation in a cooperative stochastic game with mean-variance preferences is supposed to be a random vector. We construct the core of a cooperative stochastic game with mean-variance preferences. The paper extends existing models of discrete-time stochastic games and approaches to find cooperative solutions in these games.

scholarly journals Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

2021 ◽  
Vol 24 (2) ◽  
pp. 1-35
Author(s):  
Isabel Wagner ◽  
Iryna Yevseyeva
Keyword(s):  
Optimization Problem ◽  
Evolutionary Optimization ◽  
Multiple Objectives ◽  
Shared Value ◽  
Vehicular Communications ◽  
Open Problems ◽  
Value Range ◽  
Genomic Privacy ◽  
Metaheuristic Optimization Algorithms ◽  
Selection Of

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

scholarly journals Analyzing Latency-Aware Self-Adaptation Using Stochastic Games and Simulations

2016 ◽  
Vol 10 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Javier Cámara ◽  
Gabriel A. Moreno ◽  
David Garlan ◽  
Bradley Schmerl
Keyword(s):  
Stochastic Games ◽  
Self Adaptation ◽  
Games And Simulations
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