Co-evolution of strategies for multi-objective games under postponed objective preferences

Eisenstadt, Erella; Moshaiov, Amiram; Avigad, Gideon

doi:10.1109/cig.2015.7317915

Cited by 15 publications

(19 citation statements)

References 13 publications

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“…Let the two players be denoted as P min and P max . Following Eisenstadt, Moshaiov, Avigad, and Branke (), let S min and S max be the sets of all possible I and J strategies for P min and P max , respectively, such that

\begin{matrix} true \\ S_{italicmin} = \{s_{\min}^{1} \dots s_{\min}^{i} . . s_{\min}^{I}\} \\ S_{italicmax} = \{s_{\max}^{1} \dots s_{\max}^{j} \dots s_{\max}^{J}\} \end{matrix} .

…”

Section: Solving Mogs With Undecided Objective Preferencesmentioning

confidence: 99%

“…As suggested in Eisenstadt, Moshaiov, Avigad, and Branke (2016), the associated performances can be analysed to find all rationalizable strategies, from which a strategy can be selected.…”

Section: Solving Mogs With Undecided Objective Preferencesmentioning

confidence: 99%

“…When applied on a vector, the operators (max and min) require an interpretation. Based on Eisenstadt, Moshaiov, Avigad, and Branke (2016), the meaning of these operators, for the case of a MOG under undecided objective preference, is provided in this section.…”

Section: Solving Mogs With Undecided Objective Preferencesmentioning

confidence: 99%

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Decision‐making in non‐cooperative games with conflicting self‐objectives

Eisenstadt

Moshaiov

2018

Multi Criteria Decision Anal

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This paper concerns multicriteria decision making by players in a conflict situation. The considered situation is modelled as a non‐cooperative game. Moreover, the player has a conflict not only with the opponent, but also she is faced with her own conflicting objectives. Hence, selecting a strategy is dependent on both objective preferences and the inherent uncertainty about the opponent selection. In contrast to most studies on such multiobjective games (MOGs), which employed a scalarization approach, it has recently been suggested to pose such games as MOGs with undecided objective preferences. As in Pareto optimization, the novel solution method to the considered MOGs reveals tradeoff information, which is commonly hindered when using a classical scalarization approach. To complement and highlight the significance of the aforementioned approach, two decision analysis techniques are suggested. The proposed techniques are compared with weighted‐sum utility‐based analysis, as well as with the Pareto‐optimal security strategy approach. The comparison is done using a MOG with two competing travelling salesperson. It is concluded that the suggested techniques allow incorporating tradeoff information to justify the selection of a strategy.

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\begin{matrix} true \\ S_{italicmin} = \{s_{\min}^{1} \dots s_{\min}^{i} . . s_{\min}^{I}\} \\ S_{italicmax} = \{s_{\max}^{1} \dots s_{\max}^{j} \dots s_{\max}^{J}\} \end{matrix} .

…”

Section: Solving Mogs With Undecided Objective Preferencesmentioning

confidence: 99%

“…As suggested in Eisenstadt, Moshaiov, Avigad, and Branke (2016), the associated performances can be analysed to find all rationalizable strategies, from which a strategy can be selected.…”

Section: Solving Mogs With Undecided Objective Preferencesmentioning

confidence: 99%

See 1 more Smart Citation

Decision‐making in non‐cooperative games with conflicting self‐objectives

Eisenstadt

Moshaiov

2018

Multi Criteria Decision Anal

Self Cite

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“…Section 2 presents the general formulation and fundamentals of MOGs. An overview of the baseline co-evolutionary approach for solving MOGs based on [16] is presented in Section 3. Section 4 provides a more detailed description of the proposed surrogate-assisted memetic algorithm tailored for computationally expensive MOGs.…”

Section: Introductionmentioning

confidence: 99%

Addressing expensive multi-objective games with postponed preference articulation via memetic co-evolution

Żychowski

Gupta

Mańdziuk

et al. 2018

Knowledge-Based Systems

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This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs) with postponed preference articulation can often be hampered due to the possible emergence of the so-called Red Queen effect. Accordingly, it is hypothesized that the convergence characteristics can be significantly improved through the incorporation of memetics (local solution refinements as a form of lifelong learning), as a promising means of mitigating (or at least suppressing) the Red Queen phenomenon by providing a guiding hand to the purely genetic mechanisms of co-evolution. Our practical motivation is to address MOGs of a time-sensitive nature that are characterized by computationally expensive evaluations, wherein there is a natural need to reduce the total number of true function evaluations consumed in achieving good quality solutions. To this end, we propose novel enhancements to co-evolutionary approaches for tackling MOGs, such that memetic local refinements can be efficiently applied on evolved candidate strategies by searching on computationally cheap surrogate payoff landscapes (that preserve postponed preference conditions). The efficacy of the proposal is demonstrated on a suite of test MOGs that have been designed.

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“…The optimal operating point is computed by considering a network utility function. In this model, vulnerability to the attack metric is dependent on network topology, which makes it unsuitable for fully connected networks [40].…”

Section: Pareto Optimizationmentioning

confidence: 99%

Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

et al. 2017

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Improving network security is a difficult problem that requires balancing several goals, such as defense cost and need for network efficiency, in order to achieve proper results. In this paper, we devise method of modeling network attack in a zero-sum multi-objective game and attempt to find the best defense against such an attack. We combined Pareto optimization and Q-learning methods to determine the most harmful attacks and consequently to find the best defense against those attacks. The results should help network administrators in search of a hands-on method of improving network security.

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Co-evolution of strategies for multi-objective games under postponed objective preferences

Cited by 15 publications

References 13 publications

Decision‐making in non‐cooperative games with conflicting self‐objectives

Decision‐making in non‐cooperative games with conflicting self‐objectives

Addressing expensive multi-objective games with postponed preference articulation via memetic co-evolution

Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

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