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Special Issue on optimization in Multi-Agent Systems: Guest Editor's Introduction

COMPUTER JOURNAL 2014年第6期57卷 797-798页

作者： Rodriguez-Aguilar, Juan A. Spanish Council Sci Res CSIC Artificial Intelligence Res Inst IIIA Bellaterra 08193 Spain

This special issue focuses on optimization in multi-agent systems (OptMASs), a sub-field of MASs research that poses novel research challenges. OptMASs is concerned with solving MAS problems using optimization techniques that cannot ignore the active participation of the actors in the system. Therefore, OptMASs must cope with the distributed nature of MASs, where agents reside on different computational units that may be prone to failures and have different computation/communication capabilities. Furthermore, it must consider that agents may be acting on behalf of different stakeholders, each with its own aims and objectives. Taking into account such assumptions leads to very hard optimization problems that are substantially different from problems traditionally dealt with in other areas. The objective of this special issue is to provide a detailed sample of recent advances in algorithms, theories and applications in the field of OptMASs. The seven papers included in this special issue are intended to outline the current state of the art in the field.

关键词： optimization multi-agent systems coalition formation resource allocation auctions distributed constraint optimization

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An Agent-Based distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming

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COMPUTER JOURNAL 2010年第8期53卷 1277-1290页

作者： Kho, Johnsen Tran-Thanh, Long Rogers, Alex Jennings, Nicholas R. Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding and routing actions of each node in order to maximize the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each node's energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal agent-based decentralized algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared with the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small number of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured.

关键词： distributed algorithms decentralized mechanism information metric inter-related adaptive sampling transmitting receiving forwarding and routing distributed constraint optimization

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Enhanced Frequency Response Based on Multiagent distributed Power Agreement

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2018年第2期54卷 1746-1755页

作者： Qureshi, Muhammad Umer Grijalva, Santiago Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

In this paper, we propose a multiagent based distributed algorithm for enhanced frequency response in large-scale multiarea power systems. Control nodes associated with these areas preemptively agree on their relative contributions in supporting the frequency response in a completely distributed manner. Consequently, in case of system contingencies, each area knows the total change in generation required to facilitate faster frequency restoration during primary frequency control. Within an area, local controllers associated with generators execute a distributed power agreement via sparse information exchange to achieve this required change in generation. As a result of these coordinated control actions, frequency deviation from the nominal remains minimal, thereby reducing the dependency on secondary frequency control. Inspired from graph theoretic techniques in networked controls, the proposed architecture requires nodes with minimalistic computation and communication capabilities, thereby increasing its applicability. Simulations performed on a large test case demonstrate the efficacy of the proposed algorithm in improving the transient performance of the frequency response.

关键词： distributed constraint optimization distributed control frequency response graph theory transient stability

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WHEN YOU SAY (DCOP) PRIVACY, WHAT DO YOU MEAN? Categorization of DCOP Privacy and Insights on Internal constraint Privacy

WHEN YOU SAY (DCOP) PRIVACY, WHAT DO YOU MEAN? <i>Categoriza...

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4th International Conference on Agents and Artificial Intelligence

作者： Grinshpoun, Tal SCE Sami Shamoon Coll Engn Dept Software Engn Beer Sheva Israel

ISBN: (纸本)9789898425959

Privacy preservation is a ma motivation using the DCOP model and as such, it has been the subject of comprehensive research. The present paper provides for the first lime a categorization of all possible DCOP I') types. The paper focuses on a specific type, internal constraint privacy, which is higly relevant for models that enable asynunetric payoffs (PEAV-DCOP) and ADCOP). An analysis of the run of two algorithms, one for ADCOP and one for PEAV, reveals that both models lose some internal constraint privacy.

关键词： distributed constraint optimization constraint Privacy

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Leximin Multiple Objective optimization for Preferences of Agents 1

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17th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Matsui, Toshihiro Silaghi, Marius Hirayama, Katsutoshi Yokoo, Makoto Matsuo, Hiroshi Nagoya Inst Technol Showa Ku Gokiso Cho Nagoya Aichi 4668555 Japan Florida Inst Technol Melbourne FL 32901 USA Kobe Univ Higashinada Ku Kobe Hyogo 6580022 Japan Kyushu Univ Nishi Ku Fukuoka 8190395 Japan

ISBN: (数字)9783319131917

ISBN: (纸本)9783319131917;9783319131900

We address a variation of Multiple Objective distributed constraint optimization Problems (MODCOPs). In the conventional MODCOPs, a few objectives are globally defined and agents cooperate to find the Pareto optimal solution. On the other hand, in several practical problems, the share of each agent is important. Such shares are represented as preference values of agents. This class of problems is defined as theMODCOP on the preferences of agents. Particularly, we focus on the optimization problems based on the leximin ordering (Leximin AMODCOPs), which improves the equality among agents. The solution methods based on pseudo trees are applied to the Leximin AMODCOPs.

关键词： leximin preference multiple objectives distributed constraint optimization multiagent cooperation

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Leximin Asymmetric Multiple Objective DCOP on Factor Graph 18th

Leximin Asymmetric Multiple Objective DCOP on Factor Graph

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18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Matsui, Toshihiro Silaghi, Marius Okimoto, Tenda Hirayama, Katsutoshi Yokoo, Makoto Matsuo, Hiroshi Nagoya Inst Technol Nagoya Aichi 4668555 Japan Florida Inst Technol Melbourne FL 32901 USA Kobe Univ Kobe Hyogo 6580022 Japan Kyushu Univ Fukuoka 8190395 Japan

ISBN: (纸本)9783319255248;9783319255231

Leximin AMODCOP has been proposed as a class of Multiple Objective distributed constraint optimization Problems, where multiple objectives for individual agents are optimized based on the leximin operator. This problem also relates to Asymmetric DCOPs with the criteria of fairness among agents, which is an important requirement in practical resource allocation tasks. Previous studies explore only Leximin AMODCOPs on constraint graphs limited to functions with unary or binary scopes. We address the Leximin AMODCOPs on factor graphs that directly represent n-ary functions. A dynamic programming method on factor graphs is investigated as an exact solution method. In addition, for relatively dense problems, we also investigate several inexact algorithms.

关键词： distributed constraint optimization Asymmetric Multiple objectives Leximin Egalitarian

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Multi Agent Based Railway Scheduling and optimization

Multi Agent Based Railway Scheduling and Optimization

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IEEE Region 10 Conference (TENCON)

作者： Dalapati, Poulami Singh, Arambam James Dutta, Animesh Bhattacharya, Swapan NIT Dept IT Durgapur India NIT Dept CSE Surathkal India

ISBN: (纸本)9781479940752

This paper proposes a multi agent based timetable scheduling algorithm for railway system which handles the in-between time delay of the newly introduced train. The delay management indeed optimizes the total journey time, hence increases the total utility of the whole railway system as well. Here we show that schedule generated by our proposed algorithm is the most optimized schedule. It is done by using the notion of DCOP(distributed constraint optimization Problem), where we define some metric to analyze the system to achieve our goals. We use JADE(Java Agent DEvelopment Framework) platforms to simulate our work and test it using a small network. We also take a small case study to compare our proposed work with the existing one and the results are therefore presented.

关键词： Multi agent system Railway Timetable Railway scheduling distributed constraint optimization

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Smart system to generate the optimal authorized bounding volume

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OPEN HOUSE INTERNATIONAL 2021年第3期46卷 432-443页

作者： Belkaid, Alia Ben Saci, Abdelkader Hassoumi, Ines Carthage Univ ENAU M2A Tunis Tunisia Grenoble Alpes Univ CNRS Sch Architecture AAU CRESSON Grenoble France Tunis Univ ISG SOIE COSMOS Tunis Tunisia

Purpose The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine, and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence (AI) in accomplishing this task and solving the problem. Design/methodology/approach This paper provides a method of computational design in generating the optimal authorized bounding volume which uses the best target values of morphological urban rules. It examines an intelligent system, adopting the multi-agent approach, which aims to control and increase urban densification by optimizing morphological urban rules. The process of the system is interactive and iterative. It allows collaboration and exchange between the machine and the designer. This paper is adopting and developing a new approach to resolve the distributed constraint optimization problem in generating the authorized bounding volume. The resolution is not limited to an automatic volume generation from urban rules, but also involves the production of multiple optimal-solutions conditioned both by urban constraints and relevance chosen by the designer. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the AI in accomplishing this task and solving the problem. The human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction. This paper shows and analyses interactions with the machi

关键词： Artificial intelligence Multi-agent system Authorized bounding volume distributed constraint optimization Human-computer interaction Morphological urban rules

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Reducing Bias in Preference Aggregation for Multiagent Soft constraint Problems 25th

Reducing Bias in Preference Aggregation for Multiagent Soft ...

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25th International Conference on the Principles and Practice of constraint Programming (CP)

作者： Schiendorfer, Alexander Reif, Wolfgang Univ Augsburg Inst Software & Syst Engn Augsburg Germany

ISBN: (纸本)9783030300487

Most distributed constraint optimization problems assume the overall objective function to be the "utilitarian social welfare", i.e., a sum of several utility functions, belonging to different agents. This also holds for the most popular soft constraint formalisms, cost function networks and weighted constraints. While, in theory, this model is sound, it is susceptible to manipulation and resulting bias in practice. Even without malevolent intentions, bias can result from the way orderings over solutions are transformed into numerical values or normalized. Alternatively, preferences can be aggregated directly using the tools of social choice theory to discourage manipulations and practically reduce unwanted bias. Several common voting functions can be implemented on top of constraint modeling languages through incremental search and suitable improvement predicates. We demonstrate that our approach, in particular Condorcet voting, can undo bias which is shown on two real-life-inspired case studies using the soft constraint extension MiniBrass on top of MiniZinc.

关键词： Soft constraints distributed constraint optimization Social choice Modeling languages MiniZinc

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Multiagent Based Scheduling of Elective Surgery

Multiagent Based Scheduling of Elective Surgery

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13th International Conference on Principles and Practice of Multi-Agent Systems

作者： Khanna, Sankalp Cleaver, Timothy Sattar, Abdul Hansen, David Stantic, Bela Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia Australian E Hlth Res Ctr Herston Qld 4029 Australia

ISBN: (纸本)9783642259197

Scheduling of patients, staff, and resources for elective surgery in an under-resourced and overburdened public health system represents an inherently distributed class of problems. The complexity and dynamics of interacting factors demand a flexible, reactive and timely solution, in order to achieve a high level of utilization. In this paper, we present an Automated Scheduler for Elective Surgery (ASES) wherein we model the problem using the multiagent systems paradigm. ASES is designed to reflect and complement the existing manual methods of elective surgery scheduling, while offering efficient mechanisms for negotiation and optimization. Inter-agent negotiation in ASES is powered by a distributed constraint optimization algorithm. This strategy provides hospital departments with control over their individual schedules while ensuring conflict free optimal scheduling. We evaluate ASES to demonstrate the feasibility of our approach and demonstrate the effect of fluctuation in staffing levels on theatre utilization. We also discuss ongoing development of the system, mapping key challenges in the journey towards deployment.

关键词： Multiagent Systems distributed constraint optimization

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