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A distributed multi-agent system to solve airline operations problems

A distributed multi-agent system to solve airline operations problems

Crew Recovery: In (Abdelgahny et al., 2004) the flight crew recovery problem for an airline with a hub-and-spoke (a system of air transportation in which local airports offers air transportation to a central airport where long-distance flights are available) network structure is addressed. The paper details and sub-divides the recovery problem into four categories: misplacement problems, rest problems, duty problems, and unassigned problems. Based on detailed information regarding the current plan and pool of problems, the recovery problem is solved in steps. Several means are used for recovery, including delaying, swapping, deadheading (extra- crew) and the use of stand by crew. The proposed model is an assignment model with side constraints. Due to the stepwise approach, the proposed solution is sub-optimal. Results are presented for a situation from a US airline with 18 problems.
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A multi agent based system to enable dynamic vehicle routing

A multi agent based system to enable dynamic vehicle routing

In a routing process, the time and vehicle speed re- lated data – which can be collected by telematics systems in the vehicle – are critical to determining routing anomalies. Analysis is conducted based on this data, problems are iden- tified, and solutions suggested. Novaes et al. (2012) apply a fault detection and diagnosis model to analyse data and estimate the number of pick-ups that will not be performed based on probabilities. In the event of an excessive service demand, the authors suggest that the transportation system (meaning the agents) should send out information to other vehicles operating in the area and to the warehouse asking help to perform the exceeding tasks. If there is no vehicle in the area to meet this demand, the central warehouse may appoint one or more externally owned vehicles (or third party) to perform the backlogged tasks.
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Multi-Agent System Supply Chain Management in Steel Pipe Manufacturing

Multi-Agent System Supply Chain Management in Steel Pipe Manufacturing

chain typically extends across the multiple enterprises including suppliers, manufacturers, transportation carriers, ware houses, retailers as well as customers and entails sharing forecast, order, inventory, and production information to better coordinate management decisions at multiple points throughout the extended enterprise. The basic characteristics of the supply chain indicate that the efforts associated with the formation of supply chain can be easily extended to the formation of virtual enterprise. Hence, the proposed work intends to focus on the realization of virtual enterprise through the formation of supply chain as the initial step. Agents have revolutionized manufacturing systems. Agent technology provides a natural way to design and implement distributed intelligent manufacturing environments and provides software architecture for managing the supply chain. In distributed intelligent manufacturing systems, the main function of agents is to integrate manufacturing enterprise activities such as design, planning, execution, simulation, distribution, forecasting between suppliers, customers and partners. They are also used to represent various manufacturing sources like products, parts and operations to facilitate different manufacturing activities. With this idea a research activity has been identified to develop a multi agent system model for designing supply chain which result in efficient sharing of information and integrated functioning of various units of an organization and also enhances the communication with other collaborating enterprises
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An organization based multi agent system for smarthome management

An organization based multi agent system for smarthome management

Ramchurn et al. in [35] introduce the smart grid key components: demand-side manage- ment, electric vehicles, virtual power plants, the emergence of prosumers, and self-healing networks. Demand-side management is directly related to this dissertation research, in Section 5 we describe a mechanism to manage the power consumption by the demand-side. In the paper Putting the ’smarts’ into the smart grid: a grand challenge for artificial intelligence the authors argue about the smart grid new challenges in artificial intelligence and the smart grid technologies that require algo- rithms and mechanisms to solve problems involving a large number of heterogeneous actors. There is a tendency in the developed world to decrease the use of fossil fuels and move to a low-carbon economy to guarantee energy security and mitigate the impact of energy use on the environment. This transition requires a fundamental re-thinking and re-engineering of the smart grid which must be able to make efficient use of renewable energy sources and support the additional electricity required by new actors like electrical vehicles. Many of the issues within the smart grid can be found in other domains such as water distribution, transportation, and telecommunication networks. So, there is potential to transfer technologies across these domains and also address smart grid issues that affect the sustainability of such systems.
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A Research Approach to Study Human Factors in Transportation Systems

A Research Approach to Study Human Factors in Transportation Systems

The proposed system architecture is as described in Fig- ure 1. The main module of the system is the SUMO simulator, which is responsible for the network’s multi-agent microscopic simulation, and has multiple driving agents. This module provides an overview of the whole MAS and can be manipulated directly. The SUMO module also acts as a “cen- tral server”, providing all the essential information for both IC-DEEP and the High Fidelity Simulator. This information consists of the network infrastructure and the agents in the system, whereas terrain morphology and road or building geometry are provided by the GeoStream framework. Both of the driving simulators have a local representation of the whole MAS and are capable of controlling any driving agent. The simulators are also able to connect to an Android device and pass along all the information deemed necessary, such as the GPS coordinates of the current driving agent being controlled. The Android device is running a service that receives the incoming connections from the simulator and also the ADAS being tested. Finally, the most significant modules of the system are those that allow us to conduct human factor analysis studies. We describe these modules in more detail in the next sections. The dotted area in Figure 1 corresponds to the developed components as of the writing of this paper.
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HITS: A History-Based Intelligent Transportation System

HITS: A History-Based Intelligent Transportation System

Inspired by the importance of efficient decision making along with the role of MOD in building an intelligent transportation system, we integrate both fields to develop an efficient traffic control system. Our proposed system employs historical vehicles motion patterns to answer a wide range of traffic related queries. Different moving object queries including nearest neighbor queries, range queries, along with different aggregate queries as number of moving objects in a certain region during a given time period (either in the past or the future) are all possible queries that can be efficiently answered with our proposed system.
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MAEST :Multi-Agent Environment for Software Testing

MAEST :Multi-Agent Environment for Software Testing

Communication protocol: In our system, agents of similar functionalities may have different capabilities and are implemented with different algorithms, may be executed on different platforms and specialized in dealing with different formats of information. The agent society is dynamically changing; new agents can be added into the system and old agents can be replaced by a newer version. This makes task scheduling and assignment more important and more difficult as well. Therefore, the administrator agent manages a register of agents and keeps a record of their capabilities and performances. Each agent registers its capability to the administrator when joining the system. Tests tasks are also submitted to the administrator. For each task, the administrator will send it to the most suitable agent. When an agent sends a message to the administrator, its intension must be made clear if it is to register their capabilities or to submit a test job quests, or to report the test result, etc. Such intensions are represented as 1 of the 7 illocutionary forces, which can be assertive, directive, commissive, prohibitive, declarative, or expressive.
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2 A self-organized manufacturing control architecture

2 A self-organized manufacturing control architecture

Traditionally, manufacturing control architectures relied on hierarchical organiza- tion as the mean to design those control systems. This type of organization has the advantage of collecting the information and place the processing and decisional capac- ity at central nodes that have a wider view of the system state and that are able to achieve high levels of performance optimization. At the other side, a considerable drawback can also be pointed out, related with the fact that the information processing time is high, decreasing dramatically the system responsiveness.

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Nondestructive intervention to multi-agent systems through an intelligent agent.

Nondestructive intervention to multi-agent systems through an intelligent agent.

Based on our approach, there are a number of possible future studies: (1) The goal of this paper is to prove that one intelligent shill can guide the group to synchronization and there exists such a strategy for the shill. We believe that the efficiency of the algorithm for the shill can be improved by changing the heuristics and some parameter settings. (2) In this paper, the desired heading is zero. Actually, theorem 1 is true for any desired heading h  [ ({p=2,p=2). The related ‘consistent moving’ strategy can be obtained by the state transition diagram (Fig. S1 in Appendix S1) with some minor modifications. (3) The ‘consistent moving’ strategy provides an algorithm of how a shill moves from one agent location to another without putting any negative effect on the group. This provide a base for further development of more efficient strategies for the shill, as well as strategies for other coordination purposes of Vicsek-like models, such as circus movement of group, tracking of desired route, obstacle avoidance, etc. Moreover, the strategy of the shill to lead synchronization can be a base for other coordination purpose, because it will be much easier to guide the group to turn or move to a destination after the system is synchronized; (4)The strategy for the shill in 3-D space model, and the strategy for the shill in a speed consensus problem of a continuous model that includes both magnitude and direction deserve further investigation; (5)To adding more than one intelligent shills into the group, and to design strategies for the intelligent shills to cooperate or compete for collective motions of the group will be a challenging topic.
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J. Braz. Comp. Soc.  vol.13 número2

J. Braz. Comp. Soc. vol.13 número2

In the actual software system, agent instances are au- tomatically generated based on the definition of agent classes. Each agent instance is a software entity that per- forms specific functions and also coordinates and com- municates with other agent instances. On the contrast, role classes are defined to incorporate domain knowledge and organizational relationship. Each role class is associ- ated with specific goals and detailed descriptions of how to achieve such goals. The relationships among different role classes also depict the organizational relationships among the real-world entities represented by these roles. Such information is expected to be provided by domain experts rather than software engineers. In the system run- time, role instances are created dynamically either by a human user or by agents to represent that there are cer- tain goals needed to be realized. Those role instances are mainly to carry domain knowledge and they do not actu- ally perform any actions like agents. When a role instance is taken by an agent, the agent will use the knowledge in- corporated in this role instance to achieve the goals de- fined in this role instances.
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A multi-agent system for airline operations control

A multi-agent system for airline operations control

The agent class OpMonitor is responsible for monitoring any crew events, for example, crewmembers that did not report for duty or duties with open positions, that is, without any crewmember assigned to a specific role on board (e.g., captain or flight attendant). When an event is detected, the service MonitorCrewEvents will initiate the protocol inform-crew-event (FIPA Request) informing the OpCrewFind agent. The message will include the information necessary to characterize the event. This information is passed as a serializable object of the type CrewEvent. The OpCrewFind agent detects the message and will start a CFP (call for proposal) through the crew-solution-negotiation protocol (FIPA contractNET) requesting to the specialized agents HeuristicAlgorithm, AlgorithmA and AlgorithmB (or any other that is implemented and deployed in the MAS) of any operational base of the airline company, a list of solutions for the problem. Each agent implements a different algorithm specific for this type of problem. When a solution is found a serializable object of the type CrewSolutionList is returned in the message as an answer to the CFP. The OpCrewFind agent collects all the proposals received and chooses the best one according to the algorithm in Table 2. This algorithm is implemented in the service SendCrewSolution and produces a list ordered by total cost that each solution represents. The computed values in the algorithm in Table 2 are the following:
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Object Oriented Intelligent Multi-Agent System Data Cleaning Architecture To Clean Email Data

Object Oriented Intelligent Multi-Agent System Data Cleaning Architecture To Clean Email Data

Agents are software programs that perform tasks on behalf of others and they can be used to mine data with their characteristics. Agents are task oriented with the ability to learn by themselves and they react to the situation. Learning characteristics of an agent is done by verifying its previous experience from its knowledgebase. An agent concept is a complementary approach to the Object Oriented paradigm with respect to the design and implementation of autonomous entities driven by beliefs, goals and plans. Email is one of the common means for communication via text. Email cleaning problem is formalized as non-text filtering and text normalization in a two step process. Agents incorporated in the architectural design of an Email data cleaning process combines both the features of Multi-Agent System (MAS) Framework and MAS with learning (MAS- L) Framework. MAS framework reduces the development time and the complexity of implementing the software agents. The MAS-L framework incorporates the intelligence and learning properties of software agents. MAS-L Framework makes use of the Decision Tree learning and an evaluation function to decide the next best decision that applies to the machine learning technique. This paper proposes the design for Multi- Agent based Data Cleaning Architecture that incorporates the structural design of agents into object model. The design of an architectural model for Multi-Agent based Data Cleaning inherits the features of the MAS and uses the MAS-L framework to design the intelligence and learning characteristics.
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Dynamic Composition of Service Oriented Multi-agent System in Self-organized Environments

Dynamic Composition of Service Oriented Multi-agent System in Self-organized Environments

This paper starts by referring the research efforts done on service composition in complex systems, where all network control is centralized in a single entity. Some proposed solutions, consider centralized service registrations, which leads to a bottleneck-type of problem. Taking this issue into consideration, and expecting to provide services solutions with lowest throughput costs, we introduce a truly distributed and decentralized service oriented agent-based system that positively answers this challenge. Despite the fact that decentralization brings performance benefits, it also increases the complexity, as well as the data flow to keep the distribution synchronized and coordinated. Thus, it requires behavioral and structural modifications to the system, in order to enhance the decentralization benefits. We advocate that a dynamic system capable to discover, compose through interactions and negotiation protocols, provides an answer to the service requested to be solved in a self-organized manner. As a consequence, there is a reduction of the unnecessary traffic in the network to compose or execute a service by evolving in a correct way.
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Toward an Effective Information Security Risk Management of Universities’ Information Systems Using Multi Agent Systems, Itil, Iso 27002,Iso 27005

Toward an Effective Information Security Risk Management of Universities’ Information Systems Using Multi Agent Systems, Itil, Iso 27002,Iso 27005

Abstract—Universities in the public and private sectors depend on information technology and information systems to successfully carry out their missions and business functions. Information systems are subject to serious threats that can have adverse effects on organizational operations and assets, and individuals by exploiting both known and unknown vulnerabilities to compromise the confidentiality, integrity, or availability of the information being processes, stored or transmitted by those systems. Threats to information systems can include purposeful attacks, environmental disruptions, and human/machine errors, and can result in harm to the integrity of data. Therefore, it is imperative that all the actors at all levels in a university information system understand their responsibilities and are held accountable for managing information security risk- that is the risk associated with the operation and use of information systems that support the missions and business functions of their university.
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Multi-agent System for Integrating Quality and Process Control in a Home Appliance Production Line

Multi-agent System for Integrating Quality and Process Control in a Home Appliance Production Line

The communication between the distributed agents is asynchronous and done over Ethernet network using the TCP/IP protocol. The messages exchanged by the agents are encoded using the FIPA-ACL communication language, being their content formatted according to the FIPA-SL0 language. The meaning of the message content is standardized according to the designed GRACE ontology. The integration of the GRACE ontology, edited in Protégé, in the MAS solution was performed by using the OntologyBeanGenerator plug-in, which allows to automatically generate the Java classes from the Protégé tool, following the FIPA specifications. The main generated class represents the vocabulary and main ontological objects (i.e. concepts and predicates) defined in the ontology. The second group of generated Java classes specify the structure and semantics of each ontological object defined in the ontology. The integration of legacy systems, and particularly the interaction with physical devices hosted in the production line, assumes a critical role when deploying this kind of systems into industrial environments. As an example, QCA agents are associated to quality control functions, namely to the adaptation procedures allowing the improvement of the quality control station behaviour and consequently of the whole system behaviour. The interconnection between the QCA agent and the quality control station (QCS), illustrated in Fig. 8, comprises an intelligent part (the agent) and the physical part (the hardware device responsible for the inspection tasks), which in this work also embodies a measurement system developed in LabView™.
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Cost Optimization for Series-Parallel Petroleum Transportation  Pape-Lines under Reliability Constraints

Cost Optimization for Series-Parallel Petroleum Transportation Pape-Lines under Reliability Constraints

construction of a solution, enforcing that the best features will have a more intensive pheromone. An Ant algorithm presents the following characteristics. It is a natural algorithm since it is based on the behavior of ants in establishing paths from their colony to feeding sources and back. It is parallel and distributed since it concerns a population of agents moving simultaneously, independently and without supervisor. It is cooperative since each agent chooses a path on the basis of the information, pheromone trails, laid by the other agents with have previously selected the same path. It is versatile that can be applied to similar versions the same problem. It is robust that it can be applied with minimal changes to other combinatorial optimization problems. The solution of the travelling salesman problem (TSP) was one of the first applications of ACO.
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An Agent-Based Knowledge Management System for University Research Activity Monitoring

An Agent-Based Knowledge Management System for University Research Activity Monitoring

launched by the internal clock of the system. The basic research activity information are provided by the Academic staff agents under numerical form for each research item (e.g. number of articles published in ISI journals with impact factor and relative influenced score), and as a descriptive text with all known details about the corresponding re search item (with pre defined description slots), at the scheduled monitoring time. Fig ure 6 shows an example of information that are included in the academic staff research activity report as asked by the university Vice rector.
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Integration of a multi-agent system into a robotic framework : a case study of a cooperative fault diagnosis application

Integration of a multi-agent system into a robotic framework : a case study of a cooperative fault diagnosis application

A programação de sistemas autônomos multi-robô pode ser extremamente complexa sem o uso de técnicas de desenvolvimento de software apropriadas à abstração de características de hardware, assim como pode ser difícil lidar com a complexidade de software necessária ao comporta- mento autônomo coordenado. Ambientes reais são dinâmicos e eventos inesperados podem ocorrer, levando um robô a situações não previstas ou até mesmo situações de falha. Este trabalho apresenta um método de integração do sistema multi-agente Jason com o framework robótico ROS. Através desta integração, missões complexas podem ser mais facilmete descritas tendo em vista o uso da linguagem de agentes e seus recursos, bem como a abstração de detalhes de hardware do processo de tomada de decisão. Além disso, módulos de software vinculados ao controle do hardware e mó- dulos com alto consumo de recurso de CPU são separados das rotinas de planejamento e tomada de decisão através de camadas de software, possibilitando o reuso de planos e módulos de software em diferentes missões e robôs. Através desta integração, recursos do sistema multi-agente, tais como a reconsideração de planos e planos de contingência, podem ser utilizados de forma a permitir que o robô reavalie suas ações e estratégias a fim de atingir seus objetivos ou tome ações de forma a lidar com situações imprevistas diante da dinamicidade do ambiente ou quando falhas são detectadas no hardware do robô. A integração permite ainda a cooperação entre múltiplos robôs através de uma linguagem de comunicação padronizada entre agentes. O método proposto é validado através de um estudo de caso aplicado a robôs reais onde um robô pode detectar falhas em seu hardware e diagnosticá-las através da ajuda de outro robô, em um método cooperativo de diagnóstico altamente abstrato.
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Traffic Signal Using Smart Agent System

Traffic Signal Using Smart Agent System

In fact, agent technology was begun in the 1950s. Agent is a software that user achieves automatically wanting work. In particular, this is a concept that has been studied for a long time in artificial intelligence. From the late 1980s, a boundary that is an agent has been detached with artificial intelligence and exposed to individual study subject. Agent products have appeared since the early 1990s [2] . A multi-agent system consists of multiple agents who are autonomous and make their decisions independently. By this definition, we rule out those systems where a central planner or designer controls the decision processes of local agents. If the agents’ actions do not affect each others’ outcomes, then we may as well consider the agents’ situations independently [3] . A multi-agent system offer certain advantages for problem solving: faster response,
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PRODUCTION OPTIMIZATION USING AGENT-BASED SYSTEM

PRODUCTION OPTIMIZATION USING AGENT-BASED SYSTEM

Intelligent diagnostics and maintenance – the basic objective of this approach is that any possible errors in the system to be identified at an early stage to be able to carry out activities to avoid weakening of organizational performance. In addition to the indications on the possible occurrence of errors, considered by many as the most important activities, this approach has to provide information on the type and size of errors, analysis and evaluation of potential risk and its size, as well as defining measures to resolve the problems. Thus, the approach must be rounded up to provide: identification, classification, assessment and measures to address them. In today's business conditions, each of these stages involves modern techniques especially in the field of application of artificial intelligence. Intelligent scheduling and termination – represents a process which involves the optimization in arranging the schedule of equipment and define the terms for its launch and carry out the planned functions. This is a very complex process and requires harmonization and optimization of the huge number of parameters. It often happens that certain parameters and their optima do not agree or are opposite one another and in this INTELLIGENT SYSTEM
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