A multi-agent system with application in project scheduling

The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.

An Approach to Agent-Based Service Composition and Its Application to Mobile

This paper describes an architecture model for multiagent systems that was developed in the European project LEAP (Lightweight Extensible Agent Platform). Its main feature is a set of generic services that are implemented independently of the agents and can be installed into the agents by the application developer in a flexible way. Moreover, two applications using this architecture model are described that were also developed within the LEAP project. The application domain is the support of mobile, virtual teams for the German automobile club ADAC and for British Telecommunications

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Socio-Economic Mechanisms to Coordinate the Internet of Services: The Simulation Environment SimIS

Visions of 21st century information systems show highly specialized digital services and resources, which interact continuously and with a global reach. Especially with the emergence of technologies, such as the semantic web or software agents, intelligent services within these settings can be implemented, automatically communicating and negotiating over the Internet about digital resources without human intervention. Such environments will eventually realize the vision of an open and global Internet of Services (IoS). In this paper we present an agent-based simulation model and toolkit for the IoS: 'SimIS - Simulating an Internet of Services'. Employing SimIS, distributed management mechanisms and protocols can be investigated in a simulated IoS environment before their actual deployment.Multi-Agent Simulation, Internet, Simulation Tools

Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents? payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.Fil: Tosselli, Laura. Universidad Tecnológica Nacional; ArgentinaFil: Bogado, Verónica Soledad. Universidad Tecnológica Nacional; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Aprendizaje multi-agente utilizando trial and error para la nivelación de recursos durante el (re)scheduling de múltiples proyectos

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem (RIP) is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.En un contexto de múltiples proyectos dentro de redes empresariales, alcanzar soluciones factibles al problema de (re)scheduling representa un gran desafío, principalmente al compartir recursos escasos entre proyectos. Así, el (re)scheduling de múltiples proyectos debe lograr el uso de recursos más eficiente posible sin incrementar las restricciones de proyecto planteadas, considerando el Problema de Nivelación de Recursos, cuyo objetivo es nivelar el consumo de recursos compartidos para minimizar tiempos ociosos y evitar conflictos de sobre-asignaciones. En este trabajo, una solución multi-agente para resolver el Problema de Scheduling de Múltiples Proyectos con Restricción de Recursos y el Problema de Inversión de Recursos es extendida para incorporar indicadores en las funciones de recompensa de los agentes para abordar el Problema de Nivelación de Recursos de manera autónoma y descentralizada a través de reglas desacopladas basadas en el enfoque de Aprendizaje por prueba y error. El Modelo de Simulación basado en agentes propuesto es verificado mediante un conjunto de instancias de proyecto que varían en estructura, parámetros, número de recursos compartidos, etc. Los resultados obtenidos se evalúan mediante diferentes objetivos de scheduling, como duración total del proyecto, costo total del proyecto y nivelación en el uso de recursos. Nuestros resultados presentan mejoras en comparación a los obtenidos en enfoques alternativos. Esta propuesta muestra que los agentes interactuantes que implementan reglas de aprendizaje desacopladas encuentran una solución que puede entenderse como un equilibrio de Nash.Facultad de Informátic

Aprendizaje multi-agente utilizando trial and error para la nivelación de recursos durante el (re)scheduling de múltiples proyectos

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem (RIP) is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.En un contexto de múltiples proyectos dentro de redes empresariales, alcanzar soluciones factibles al problema de (re)scheduling representa un gran desafío, principalmente al compartir recursos escasos entre proyectos. Así, el (re)scheduling de múltiples proyectos debe lograr el uso de recursos más eficiente posible sin incrementar las restricciones de proyecto planteadas, considerando el Problema de Nivelación de Recursos, cuyo objetivo es nivelar el consumo de recursos compartidos para minimizar tiempos ociosos y evitar conflictos de sobre-asignaciones. En este trabajo, una solución multi-agente para resolver el Problema de Scheduling de Múltiples Proyectos con Restricción de Recursos y el Problema de Inversión de Recursos es extendida para incorporar indicadores en las funciones de recompensa de los agentes para abordar el Problema de Nivelación de Recursos de manera autónoma y descentralizada a través de reglas desacopladas basadas en el enfoque de Aprendizaje por prueba y error. El Modelo de Simulación basado en agentes propuesto es verificado mediante un conjunto de instancias de proyecto que varían en estructura, parámetros, número de recursos compartidos, etc. Los resultados obtenidos se evalúan mediante diferentes objetivos de scheduling, como duración total del proyecto, costo total del proyecto y nivelación en el uso de recursos. Nuestros resultados presentan mejoras en comparación a los obtenidos en enfoques alternativos. Esta propuesta muestra que los agentes interactuantes que implementan reglas de aprendizaje desacopladas encuentran una solución que puede entenderse como un equilibrio de Nash.Facultad de Informátic