The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerou...
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The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology.
Managing scientific applications in the Cloud poses many challenges in terms of workflow scheduling, especially in handling multi-objective workflow scheduling under quality of service (QoS) constraints. However, most...
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Managing scientific applications in the Cloud poses many challenges in terms of workflow scheduling, especially in handling multi-objective workflow scheduling under quality of service (QoS) constraints. However, most studies address the workflow scheduling problem on the premise of the unchanged environment, without considering the high dynamics of the Cloud. In this paper, we model the constrained workflow scheduling in a dynamic Cloud environment as a dynamic multi-objective optimization problem with preferences, and propose a transfer learning based multi-objective evolutionary algorithm (TL-MOEA) to tackle the workflow scheduling problem of dynamic nature. Specifically, an elite-led transfer learning strategy is proposed to explore effective parameter adaptation for the MOEA by transferring helpful knowledge from elite solutions in the past environment to accelerate the optimization process. In addition, a multi-space diversity learning strategy is developed to maintain the diversity of the population. To satisfy various QoS constraints of workflow scheduling, a preference-based selection strategy is further designed to enable promising solutions for each iteration. Extensive experiments on five well-known scientific workflows demonstrate that TL-MOEA can achieve highly competitive performance compared to several state-of-art algorithms, and can obtain triple win solutions with optimization objectives of minimizing makespan, cost and energy consumption for dynamic workflow scheduling with user-defined constraints.
With the continuous urbanization and industrialization in recent years, different stakeholders in the same basin meet local water needs and compete for water resources, which hinders economic development and ecologica...
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With the continuous urbanization and industrialization in recent years, different stakeholders in the same basin meet local water needs and compete for water resources, which hinders economic development and ecological protection of the basin. Different stakeholders conceal information about their own strategies and preferences to protect their vested interests, making it difficult for third-party regulators to develop optimal operational policies for the joint use of water resources in the basin. Therefore, the research objective of this study is to propose a new grey inverse Graph Model for Conflict Resolution (grey inverse GMCR), and apply the new method to the complex problem of water allocation and environmental governance with the Poyang Lake basin conflict. The new GMCR model proposed in this study can solve the cost minimization problem of a third-party mediator that manipulates decision makers (DMs)' grey preferences to achieve desired outcomes. The results of the equilibrium analysis show that upstream areas' preferences mainly affect the result of water resource conflicts in the PLB, and establishing a scientific mechanism to compensate the water resources protection cost borne by the upstream areas would be a cost-effective way to realize the sustainable development of the basin as a whole.
We introduce a new conversation head generation benchmark for synthesizing behaviors of a single interlocutor in a face-to-face conversation. The capability to automatically synthesize interlocutors which can particip...
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Much like relational probabilistic models, the need for relational preference models naturally arises in real-world applications involving multiple, heterogeneous, and richly interconnected objects. On the one hand, r...
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Much like relational probabilistic models, the need for relational preference models naturally arises in real-world applications involving multiple, heterogeneous, and richly interconnected objects. On the one hand, relational preferences should be represented into statements which are natural for human users to express. On the other hand, relational preference models should be endowed with a structure that supports tractable forms of reasoning and learning. Based on these criteria, this paper introduces the framework of relational conditional preference networks (RCP-nets), that maintains the spirit of the popular "CP-nets" by expressing relational preferences in a natural way using the ceteris paribus semantics. We show that acyclic RCP-nets support tractable inference for optimization and ranking tasks. In addition, we show that in the online learning model, tree-structured RCP-nets (with bipartite orderings) are efficiently learnable from both optimization tasks and ranking tasks, using linear loss functions. Our results are corroborated by experiments on a large-scale movie recommendation dataset.
This paper concerns a static multi-objective control problem of the distributed parameter system, in which regionally decentralized plural decision-makers carry out control actions based on their own goals. The proble...
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This paper concerns a static multi-objective control problem of the distributed parameter system, in which regionally decentralized plural decision-makers carry out control actions based on their own goals. The problem is formulated as minimization of vector functional to generate noninferior controls. Optimality conditions are derived. Moreover, we study a multi-objective decision problem to choose a preference optimal solution from among a set of non-inferior controls. We consider the following two cases; a) there exists a central decision-maker in the upper level (a central decision problem), and b) no central decision-maker exists and a collective decision is made (a collective decision problem). Two-level computational procedures using a constrained simplex method and an interior penalty method are presented for both cases. Gradient method is used for optimizing a system governed by partial differential equations.
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