Multi-agent System for Resource Allocation and Scheduling

Gorodetski, Vladimir I.; Karsaev, Oleg; Konushy, Victor

doi:10.1007/3-540-45023-8_23

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…Resource allocation in multi-agent systems is a well-studied area (e.g., (Chevaleyre et al 2006;Gorodetski, Karsaev, and Konushy 2003;Dolgov and Durfee 2006)). The general focus of this work is on topics such as how agents express their requirements, algorithms for allocating resources and evaluating the quality of the resulting allocations.…”

Section: Related Workmentioning

confidence: 99%

Resource Sharing through Multi-Round Matchings

Trabelsi

Adiga

Kraus

et al. 2023

AAAI

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Applications such as employees sharing office spaces over a workweek can be modeled as problems where agents are matched to resources over multiple rounds. Agents' requirements limit the set of compatible resources and the rounds in which they want to be matched. Viewing such an application as a multi-round matching problem on a bipartite compatibility graph between agents and resources, we show that a solution (i.e., a set of matchings, with one matching per round) can be found efficiently if one exists. To cope with situations where a solution does not exist, we consider two extensions. In the first extension, a benefit function is defined for each agent and the objective is to find a multi-round matching to maximize the total benefit. For a general class of benefit functions satisfying certain properties (including diminishing returns), we show that this multi-round matching problem is efficiently solvable. This class includes utilitarian and Rawlsian welfare functions. For another benefit function, we show that the maximization problem is NP-hard. In the second extension, the objective is to generate advice to each agent (i.e., a subset of requirements to be relaxed) subject to a budget constraint so that the agent can be matched. We show that this budget-constrained advice generation problem is NP-hard. For this problem, we develop an integer linear programming formulation as well as a heuristic based on local search. We experimentally evaluate our algorithms on synthetic networks and apply them to two real-world situations: shared office spaces and matching courses to classrooms.

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Section: Related Workmentioning

confidence: 99%

Resource Sharing through Multi-Round Matchings

Trabelsi

Adiga

Kraus

et al. 2023

AAAI

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“…In the multi-agent literature, the use of market based allocation procedures is the main approach adopted to solve VRPTW. These procedures are often based on Contract Net Protocol (Davis and Smith, 1983;Fischer et al, 1999) or on Vickrey auction model (Vickrey, 1961;Gorodetski et al, 2003), but only few propose an approach for dynamic task reallocation. Fischer et al (Fischer et al, 1999) presented a multi-agent simulation framework where agents interactions are driven by negotiation protocols.…”

Section: Our Problemmentioning

confidence: 99%

A Multi-Agent Model Based on Residual Resources

Hette

Estivie

Adam

et al. 2012

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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We propose a model to solve the disturbances which could occur during the execution of agents task schedules in a dynamic multi-agent system. Our model is based on pooling residual resources to reallocate tasks in case of incoming tasks or disturbances during the execution of the system. In our context, initial task schedules of agents are defined such that, during some given time windows, agents can perform additional tasks with their residual resources. In this paper, we propose a model for this problem. In order to validate this model, we also propose a first resolution method attempt and an experimental study of this framework.

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“…This software tool was the subject of research during last years and the current version is the third one. Previous versions of MASDK [13] were used for rapid prototyping of different MAS applications [14,15]. The experience accumulated so far allowed understanding of the drawbacks and limitations of the previous two versions of MASDK and developing adequate requirements to its current version, 3.0.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent System Development Kit

Gorodetski

Karsaev

Samoilov

et al. 2005

Intelligent Information Processing II

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Abstract:Recent research in area of multi-agent technology attracted a growing attention of both scientific community and industrial companies. This attention is stipulated by powerful capabilities of multi-agent technology allowing to create large scale distributed intelligent systems, and, on the other hand, by practical needs of industrial companies to possess an advanced and reliable technology for solving of practically important problems. Currently one of the topmost questions of the research is development of powerful methodologies for engineering of agent-based systems and development of more effective and efficient tools supporting implementation of applied systems. The paper presents one of such tools, Multi Agent System Development Kit, based on and implementing of Gaia methodology. It supports the whole life cycle of multi-agent system development and maintains integrity of solutions produced at different stages of the development process.

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Multi-agent System for Resource Allocation and Scheduling

Cited by 8 publications

References 4 publications

Resource Sharing through Multi-Round Matchings

Resource Sharing through Multi-Round Matchings

A Multi-Agent Model Based on Residual Resources

Multi-Agent System Development Kit

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