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Hazards XVII Symposium

作者： Busby, JS Hughes, E Terry, E Sharp, JV Strutt, JE Lemon, M Univ Bath Bath BA2 7AY Avon England

ISBN: (纸本)085295459X

The distribution of people's problem solving is an important source of failure in complex, hazardous production systems. An analysis was made of about 60 offshore accidents and incidents in an attempt to work out how people distribute their problem solving and how this fails. Inferences were made about the assumptions that were effectively being made in this distribution, and how the system could have been made less vulnerable to such assumptions in the design process. A prompting tool has been developed to help introduce this analysis into risk identification exercises - in both design and operations.

关键词： human error distributed problem solving assumptions

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Research of a construction method of space-based cyber-physical system 28

Research of a construction method of space-based cyber-physi...

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28th Chinese Control and Decision Conference, CCDC 2016

作者： Zhongcheng, Yuan Fengge, Wu Junsuo, Zhao Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781467397148

Cyber-physical systems (CPS) are integrations of computation and physical processes. For space environment, this paper proposes space-based cyber-physical systems (SCPS), which are intelligent systems that can satisfy the rising demand for space exploration. Compared with traditional satellite system, SCPS can be built with lower cost, shorter development cycle and can accomplish diverse tasks. In order to build SCPS, Sandroid architecture and Sandroid satellite are introduced. And the network construction of SCPS is described. Sandroid satellite is a kind of CubeSat uses Android as operating system and smartphone chips as control unit. In SCPS, Sandroid satellites collaborate with each other to accomplish complex task. So a new method for the collaboration among Sandroid satellites is presented. © 2016 IEEE.

关键词： CubeSat Cyber-physical System distributed problem solving Multi-agent system Sandroid

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Incomplete distributed Constraint Optimization problems: Model, Algorithms, and Heuristics 1

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3rd International Conference on distributed Artificial Intelligence, DAI 2021

作者： Tabakhi, Atena M. Yeoh, William Zivan, Roie Washington University in St. Louis St. Louis United States Ben Gurion University of the Negev Beer Sheva Israel

ISBN: (数字)9783030946623

ISBN: (纸本)9783030946616

The distributed Constraint Optimization problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems, especially when they are sparsely constrained with one another. A key assumption in this model is that all constraints are fully specified or known a priori, which may not hold in applications where constraints encode preferences of human users. In this paper, we extend the model to Incomplete DCOPs (I-DCOPs), where some constraints can be partially specified. User preferences for these partially-specified constraints can be elicited during the execution of I-DCOP algorithms, but they incur some elicitation costs. Additionally, we extend SyncBB, a complete DCOP algorithm, and ALS-MGM, an incomplete DCOP algorithm, to solve I-DCOPs. We also propose parameterized heuristics that those algorithms can utilize to trade off solution quality for faster runtime and fewer elicitation. They also provide theoretical quality guarantees when used by SyncBB when elicitations are free. Our model and heuristics thus extend the state-of-the-art in distributed constraint reasoning to better model and solve distributed agent-based applications with user preferences. © 2022, Springer Nature Switzerland AG.

关键词： distributed constraint optimization problems distributed problem solving Multi-agent problems Preference elicitation

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Crossbar Composite Spring-Nets to Optimize Multi-Agent Systems and Computer Networks

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4th International Conference on Intelligent Computing

作者： Shuai, Dianxun E China Univ Sci & Technol Shanghai 200237 Peoples R China

ISBN: (纸本)9783540859833

Elastic nets (EN) have been effectively used in the traveling salesman problem (TSP), the prediction problem of protein structure, and so on.(1) Nevertheless the limitations of the EN theory and its architecture seriously prevent the EN approach from the application for problem-solving of multi-agent systems (MAS) and computer networks (CN). This paper presents a crossbar composite spring-nets (CCSN) approach to MAS and CN, which transforms the optimization problem of MAS and CN into the evolutionary dynamics of crossbar composite spring nets. The CCSN approach is essentially different from EN and has many advantages over EN in terms of the problem-solving performance and the suitability for complex environment in MAS and CN.

关键词： elastic nets crossbar composite spring nets artificial intelligence multi-agent system distributed problem solving

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Combining search strategies for distributed constraint satisfaction

Combining search strategies for distributed constraint satis...

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作者： Magaji, Amina Sambo-Muhammad Robert Gordon University

学位级别：master

Many real-life problems such as distributed meeting scheduling, mobile frequency allocationand resource allocation can be solved using multi-agent paradigms. distributed constraint satisfactionproblems (DisCSPs) is a framework for describing such problems in terms of relatedsubproblems, called a complex local problem (CLP), which are dispersed over a number of locations,each with its own constraints on the values their variables can take. An agent knows thevariables in its CLP plus the variables (and their current value) which are directly related to oneof its own variables and the constraints relating them. It knows little about the rest of the ***, each CLP is solved by an agent which cooperates with other agents to solve the *** for solving DisCSPs can be classified as either systematic or local search withthe former being complete and the latter incomplete. The algorithms generally assume that eachagent has only one variable as they can solve DisCSP with CLPs using “virtual” agents. However,in large DisCSPs where it is appropriate to trade completeness off against timeliness, systematicsearch algorithms can be expensive when compared to local search algorithms which generallyconverge quicker to a solution (if a solution is found) when compared to systematic algorithms. Amajor drawback of local search algorithms is getting stuck at local optima. Significant researcheshave focused on heuristics which can be used in an attempt to either escape or avoid local *** thesis makes significant contributions to local search algorithms for DisCSPs. Firstly, wepresent a novel combination of heuristics in DynAPP (Dynamic Agent Prioritisation with Penalties),which is a distributed synchronous local search algorithm for solving DisCSPs having onevariable per agent. DynAPP combines penalties on values and dynamic agent prioritisation heuristicsto escape local optima. Secondly, we develop a divide and conquer approach that handlesDisCSP with

关键词： distributed constraint satisfaction distributed problem solving Local search Algorithms Local optima Heuristics

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Research of a Construction Method of Space-based Cyber-Physical System

Research of a Construction Method of Space-based Cyber-Physi...

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第28届中国控制与决策会议

作者： Yuan Zhongcheng Wu Fengge Zhao Junsuo Science and Technology on Integrated Information System Laboratory Institute of SoftwareChinese Academy of Sciences

ISBN: (纸本)9781467397155

Cyber-physical systems(CPS) are integrations of computation and physical *** space environment,this paper proposes space-based cyber-physical systems(SCPS),which are intelligent systems that can satisfy the rising demand for space *** with traditional satellite system,SCPS can be built with lower cost,shorter development cycle and can accomplish diverse *** order to build SCPS,Sandroid architecture and Sandroid satellite are *** the network construction of SCPS is *** satellite is a kind of Cube Sat uses Android as operating system and smartphone chips as control *** SCPS,Sandroid satellites collaborate with each other to accomplish complex *** a new method for the collaboration among Sandroid satellites is presented.

关键词： Cube Sat Sandroid Cyber-physical System Multi-agent system distributed problem solving

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Applicability of Multi-Agent Systems and Constrained Reasoning for Sensor-Based distributed Scenarios: A Systematic Mapping Study on Dynamic DCOPs

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SENSORS 2021年第11期21卷 3807-3807页

作者： Barambones, Jose Imbert, Ricardo Moral, Cristian Univ Politecn Madrid Madrid Human Comp Lab Boadilla Del Monte 28660 Spain

Context: At present, sensor-based systems are widely used to solve distributed problems in changing environments where sensors are controlled by intelligent agents. On Multi-Agent Systems, agents perceive their environment through such sensors, acting upon that environment through actuators in a continuous cycle. These problems have not always been addressed from an ad-hoc perspective, designed specifically for the circumstances of the problem at hand. Instead, they have been modelled under a common mathematical framework as distributed constrained optimisation problems (DCOP). Objective: The question to answer is how sensor-based scenarios have been modelled as DCOPs in changing environments known as Dynamic DCOP and what their trends, gaps, and progression are. Method: A systematic mapping study of Dynamic DCOPs has been conducted, considering the scattered literature and the lack of consensus in the terminology. Results: Given the high complexity of distributed constraint-based problems, priority is given to obtaining sub-optimal but fast responses with a low communication cost. Other trending aspects are the scalability and guaranteeing the solution over time. Conclusion: Despite some lacks in the analysis and experimentation in real-world scenarios, a large set that is applicable to changing sensor-based scenarios is evidenced, along with proposals that allow the integration of off-the-shell constraint-based algorithms.

关键词： intelligent sensor networks distributed problem solving sensors on dynamic environments systematic mapping study distributed constraint optimisation problem

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A particle swarm inspired approach for continuous distributed constraint optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Choudhury, Moumita Sarker, Amit Yaser, Samin Khan, Md. Maruf Al Alif Yeoh, William Khan, Md. Mosaddek Univ Massachusetts Amherst Coll Informat & Comp Sci Amherst MA USA Washington Univ St Louis Dept Comp Sci & Engn 1 Brookings Dr St Louis MO 63130 USA Univ Dhaka Dept Comp Sci & Engn Sci Complex Dhaka 1000 Bangladesh

distributed Constraint Optimization problems (DCOPs) are a widely studied framework for coordinating in-teractions in cooperative multi-agent systems. In classical DCOPs, variables owned by agents are assumed to be discrete. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous-valued variables are more suitable than discrete ones. To better model such applications, researchers have proposed Continuous DCOPs (C-DCOPs), an extension of DCOPs, that can explicitly model problems with continuous variables. The state-of-the-art approaches for solving C-DCOPs experience either onerous memory or computation overhead and are unsuitable for non-differentiable optimization problems. To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population -based approach for solving continuous optimization problems. In recent years, population-based algorithms have gained significant attention in classical DCOPs due to their ability in producing high-quality solutions. Nonetheless, to the best of our knowledge, this class of algorithms has not been utilized to solve C-DCOPs and there has been no work evaluating the potential of PSO in solving classical DCOPs or C-DCOPs. In light of this observation, we adapted PSO, a centralized algorithm, to solve C-DCOPs in a decentralized manner. The resulting PCD algorithm not only produces good-quality solutions but also finds solution without any requirement for derivative calculations. Moreover, we design a crossover operator that can be used by PCD to further improve the quality of solutions found. Finally, we theoretically prove that PCD is an anytime algorithm and empirically evaluate PCD against the state-of-the-art C-DCOP algorithms in a wide variety of benchmarks.

关键词： Population-based algorithms distributed problem solving DCOPs Multi-agent systems

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A Knowledge Based System for Stability Robustness Analysis and Robust Control Design: a Case Study of Nuclear Power Plant Control

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IFAC Proceedings Volumes 1988年第19期21卷 35-45页

作者： D.B. Petkoviski Centre for Large Scale Control and Decision Systems Institute for Computers Control and Measurement Faculty of Technical Sciences Novi Sad Yugoslavia

The paper gives a conceptual framework for stability robustness analysis and robust decentralized control system design, and its realization through an interactive computer-aided software. The system combines algorithmic and expert system techniques. An important feature of the presented knowledqe based system is the modularization of the software package which allows a distributed problem solving approach. The 15th order mathematical model of a nuclear power plant which includes the physical processes, as well as the main control systems, is used to demonstrate the power of the proposed knowledge based methodology

关键词： Artifical intelligence Knowledqe based systems distributed problem solving stability robustness Robust decentralized systems Nuclear power plant

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A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems

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Procedia Computer Science 2010年第1期1卷 2217-2226页

作者： Predrag T. Tošić Ricardo Vilalta Department of Computer Science University of Houston Houston Texas USA

Coordination among multiple autonomous, distributed cognitive agents is one of the most challenging and ubiquitous problems in distributed AI and its applications in general, and in collaborative multi-agent systems in particular. A particularly prominent problem in multi-agent coordination is that of group, team or coalition formation. A considerable majority of the approaches to this problem found in the literature assume fixed interactions among autonomous agents involved in the coalition formation process. Moreover, most of the prior research where agents are actually able to learn and adapt based on their past interactions mainly focuses on reinforcement learning techniques at the individual agent level. We argue that, in many important applications and contexts, complex large-scale collaborative multi-agent systems need to be able to learn and adapt at multiple organization, hierarchical and logical levels. In particular, the agents need to be able to learn both at the level of individual agents and at the system or agent ensemble levels, and then to integrate these different sources of learned knowledge and behavior, in order to be effective at solving complex tasks in typical dynamic, partially observable and noisy multi-agent environments. In this paper, we describe a conceptual framework for addressing the problem of learning how to coordinate effectively at three qualitatively distinct levels — those of (i) individual agents, (ii) small groups of agents, and (iii) very large agent ensembles (or alternatively, depending on the nature of a multi-agent system, at the system or central control level). We briefly illustrate the applicability and usefulness of the proposed conceptual framework with an example of how it would apply to an important practical coordination problem, namely that of distributed coordination of a large ensemble of unmanned vehicles on a complex multi-task mission.

关键词： Autonomous agents Multi-agent systems distributed problem solving Agent coordination Coalition formation distributed task allocation Reinforcement learning Co-learning Meta-learning Multi-level learning

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