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13th Annual International Workshop on constraint Solving and constraint Logic Programming

作者： Yeoh, William Felner, Ariel Koenig, Sven Univ So Calif Los Angeles CA 90089 USA Ben Gurion Univ Negev Dept Informat Engn IL-85104 Beer Sheva Israel

ISBN: (纸本)9783642032509

Many agent coordination problems can be modeled as distributed constraint optimization (DCOP) problems. ADOPT is an asynchronous and distributed search algorithm that is able to solve DCOP problems optimally. In this paper, we introduce Iterative Decreasing Bound ADOPT (IDB-ADOPT), a modification of ADOPT that changes the search strategy of ADOPT from performing one best-first search to performing a series of depth-first searches. Each depth-first search is provided with a bound, initially a, large integer, and returns the first solution whose cost is smaller than or equal to the bound. The bound is then reduced to the cost of this solution minus one and the process repeats. If there is no solution whose cost is smaller than or equal to the bound, it returns a. cost-minimal solution. Thus, IDB-ADOPT is an anytime algorithm that solves DCOP problems with integer costs optimally. Our experimental results for graph coloring problems show that IDB-ADOPT runs faster (that is, needs fewer cycles) than ADOPT oil large DCOP problems, with savings of up to one order of magnitude.

关键词： ADOPT DCOP distributed constraint optimization distributed Search Algorithms

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A Large Neighboring Search Schema for Multi-agent optimization 1

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24th International Conference on the Principles and Practice of constraint Programming (CP)

作者： Hoang, Khoi D. Fioretto, Ferdinando Yeoh, William Pontelli, Enrico Zivan, Roie Washington Univ St Louis MO 63110 USA Univ Michigan Ann Arbor MI 48109 USA New Mexico State Univ Las Cruces NM 88003 USA Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (数字)9783319983349

ISBN: (纸本)9783319983349;9783319983332

The distributed constraint optimization Problem (DCOP) is an elegant paradigm for modeling and solving multi-agent problems which are distributed in nature, and where agents cooperate to optimize a global objective within the confines of localized communication. Since solving DCOPs optimally is NP-hard, recent effort in the development of DCOP algorithms has focused on incomplete methods. Unfortunately, many of such proposals do not provide quality guarantees or provide a loose quality assessment. Thus, this paper proposes the distributed Large Neighborhood Search (DLNS), a novel iterative local search framework to solve DCOPs, which provides guarantees on solution quality refining lower and upper bounds in an iterative process. Our experimental analysis of DCOP benchmarks on several important classes of graphs illustrates the effectiveness of DLNS in finding good solutions and tight upper bounds in both problems with and without hard constraints.

关键词： Multiagent Systems distributed constraint optimization Large Neighborhood Search

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An Intelligent Approach to Surgery Scheduling

An Intelligent Approach to Surgery Scheduling

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

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

ISBN: (纸本)9783642259197

The Multiagent Systems paradigm offers expressively rich and natural fit mechanisms for modeling and negotiation for solving distributed problems. Solving complex and distributed real world problems in dynamic domains however presents a significant challenge and requires the integration of technology innovation and domain expertise to create intelligent solutions. Scheduling of patients, staff, and resources for elective surgery in an under-resourced and overburdened public health system presents an excellent example of this class of problems. In this paper, we discuss the research challenges presented by the problem and outline our efforts of applying distributed constraint optimization;intelligent decision support, and prediction based theater allocation to address these challenges. We also discuss how these technologies can be used to drive better planning and change management in the context of surgery scheduling.

关键词： Multiagent Systems distributed constraint optimization

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distributed Air Traffic Flow Management at Terminal Control Area 8

Distributed Air Traffic Flow Management at Terminal Control ...

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8th International Conference on Cyber-Enabled distributed Computing and Knowledge Discovery (CyberC)

作者： Yang, Xiaolei Zhao, Xuejun Yuan, Xiujiu Air Force Engn Univ Sch Air & Missile Def Changle Rd 1 Xian 710051 Peoples R China

ISBN: (纸本)9781509051540

With the growing air traffic demand and limited air traffic infrastructures, the airport is becoming the bottleneck of the whole air traffic flow management system and the air traffic flow management at airport has been a hot issue for a few years. In this paper, we for the first time model the ATC-TCA problem as a distributed constraint optimization problem and solve it with both complete and incomplete DCOP algorithms. Simulation results verify the effectiveness and efficiency of the proposed approach.

关键词： distributed constraint optimization air traffic flow management routing scheduling

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Multi-Agent Restoration Process Based on distributed optimization Search

Multi-Agent Restoration Process Based on Distributed Optimiz...

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2017 IEEE Power & Energy Society General Meeting

作者： El-Sharafy, M. Zaki Farag, H. E. York Univ Lassonde Sch Engn Elect Engn & Comp Sci Toronto ON Canada

ISBN: (纸本)9781538622124

This paper proposes a new multiagent control algorithm for automatic back-feed service restoration in smart distribution grids. The aim of the proposed algorithm is to obtain the advantages of both centralized and distributed control schemes by installing a less acceptable number of installed agents. The objective of these agents is to minimize the unserved loads and the total number of switching operations, taking into consideration the load variability. The proposed algorithm adopts the Optimal Asynchronous Partial Overlay (OPTAPO) technique, which is based on the distributed constraint agent search in order to obtain the back-feed restoration process in a multi-agent environment. Case studies are simulated to evaluate the effectiveness of the proposed algorithm.

关键词： Smart distribution grids Self-healing restoration distributed constraint optimization distributed generation (DG) Variable Loads

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Scalable Antenna Orientation optimization for mmWave Mobile Communication Systems

Scalable Antenna Orientation Optimization for mmWave Mobile ...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Shen, Linzhi Wang, Shaowei Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China

ISBN: (纸本)9798350310900

Tuning the azimuths and tilts of antennas in an optimal way is crucial for the mmWave mobile communication systems, nevertheless, it always yields an intractable combinatorial optimization problem due to the huge number of possible antenna configurations. In this paper, we propose a scalable reinforcement learning method to deal with this optimization task, which decomposes and distributes the policy learning process by exploiting the interactions among adjacent antennas implicitly. A distributed constraint optimization technique is introduced to coordinate the distributed learning process, which can lead the learning towards desired directions. Experiment results in large-scale scenarios demonstrate that our proposed method yields significant performance improvement in terms of signal-to-interference ratio coverage while ensuring high power coverage.

关键词： Antenna orientation optimization coordinated reinforcement learning distributed constraint optimization mmWave mobile communications

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Enhanced Frequency Response Based on Multiagent distributed Power Agreement

Enhanced Frequency Response Based on Multiagent Distributed ...

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9th Annual IEEE Green Technologies Conference (GreenTech)

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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Near-Optimal Decentralized Power Supply Restoration in Smart Grids 14

Near-Optimal Decentralized Power Supply Restoration in Smart...

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14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Agrawal, Pritee Kumar, Akshat Varakantham, Pradeep Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450334136

Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust operation in the face of disasters. Such heterogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, provides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, enabling decentralized control. Using these quality bounds, we show that our approach can provide near-optimal solutions on a number of large real-world and synthetic benchmarks. Our approach compares favorably both in solution quality and scalability with previous best multiagent PSR approach.

关键词： Multi-agent Systems distributed constraint optimization Power restoration Smart Grids

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Governing convergence of Max-sum on DCOPs through damping and splitting

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ARTIFICIAL INTELLIGENCE 2020年 279卷

作者： Cohen, Liel Galiki, Rotem Zivan, Roie Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

Max-sum is a version of Belief Propagation, used for solving DCOPs. In tree-structured problems, Max-sum converges to the optimal solution in linear time. Unfortunately, when the constraint graph representing the problem includes multiple cycles (as in many standard DCOP benchmarks), Max-sum does not converge and explores low quality solutions. Recent attempts to address this limitation proposed versions of Max-sum that guarantee convergence, while ignoring some of the problem's constraints. Damping is a method that is often used for increasing the chances that Belief Propagation will converge. That being said, it has not been suggested for inclusion in the algorithms that propose Max-sum for solving DCOPs. In this paper we advance the research on incomplete-inference DCOP algorithms by: 1) investigating the effect of damping on Max-sum. We prove that, while damping slows down the propagation of information among agents, on tree-structured graphs, Max-sum with damping is guaranteed to converge to the optimal solution in weakly polynomial time;and 2) proposing a novel method for adjusting the level of asymmetry in the factor graph, in order to achieve a balance between exploitation and exploration, when using Max-sum for solving DCOPs. By converting a standard factor graph to an equivalent split constraint factor graph (SCFG), in which each function-node is split into two function nodes, we can control the level of asymmetry for each constraint. Our empirical results demonstrate a drastic improvement in the performance of Max-sum when using damping (referred to herein as Damped Max-sum, DMS). However, in contrast to the common assumption that Max-sum performs best when converging, we demonstrate that non converging versions perform efficient exploration, and produce high quality results, when implemented within an anytime framework. On most standard benchmarks, the best results were achieved using versions with a high damping factor, which outperformed existing incom

关键词： distributed constraint optimization Incomplete inference algorithms Max-sum

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Scheduling operations in a large hospital by multiple agents

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Gaon, Noam Schlosberg, Yuval Gabai Zivan, Roie Ben Gurion Univ Negev Ind Engn & Management Beer Sheva Israel

The scheduling of operations in a large hospital is performed jointly by several groups of people, each with its own objective and constraints. It is a two-phase process, starting with the allocation of operating rooms to wards, and followed by the scheduling of operations in each operating room of the hospital on each day. The final schedule must satisfy all inter-ward hard constraints, such as the allocation of anesthetists, nurses, and equipment to operations that are taking place in parallel, and ideally, it should also address soft constraints such as taking the urgency and complexity of operations into consideration. This study contributes to the ongoing effort of adapting multi-agent optimization models and algorithms to real-world applications by modeling the problems in both phases as distributed constraint optimization problems (DCOPs), with different properties. The first phase includes partially cooperative ward-representing agents, allocating operating rooms for daily usage among themselves. In the second phase, ward-representing agents interact with agents representing constraining elements, in order to generate daily operation schedules for each operating room, thus forming a unique bipartite constraint graph. On one side are the ward representatives, while on the other are the agents representing the constraining resources. Each agent has a non-trivial local problem to solve, and its solution serves as the proposed assignment in the distributed algorithm. The study begins by discussing the properties required of the algorithms needed to solve the two phases. It then proposes adjustments to existing distributed partially cooperative algorithms and local search algorithms to solve these problems, and compares the results of different variants of these algorithms. The results obtained for both phases emphasize that successful collaboration is predicated on two requirements: that agents hold consistent information regarding their peers' states and that t

关键词： distributed constraint optimization Multi-agent system Multi-agent applications Operating-room scheduling distributed local search algorithms

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