AML Modeling of Trust in Supply Chain Management

Agent Modeling Language (AML) is a semiformal visual modeling language for specifying, modeling and documenting systems in terms of concepts drawn from Multi Agent Modeling (MAS) theory. Supply chain management (SCM) is the management of network of interconnected business which spans all movements of services and goods from the point of origin to the point of consumption. In SCM, Trust modeling is an important and crucial aspect from the perspective of sustainability of the supply chain and efficient performance in business. In the supply chain, the more we trust, the more we exchange information on demand and on forecast of the last customer so as with the level of stock and on the forecast of the suppliers. In this work, we attempted to model the Trust in SCM using AML and proposed a MAS SCM model of trust in supply chain management. The proposed model is implemented using JADE and the simulation results demonstrated the impact of trust in supply chain along with the evolution of trust

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Coordination Of Hierarchical Command And Control Services

The purpose of this program is to show emerging information technologies can significantly improve key areas of tactical operations, resulting in the conversion of software developed under the ATO to existing battlefield systems. One such key area is Information Dissemination and Management (ID&M). The key software that will be developed under the ID&M portion requires a collection of agent-based software services that will collaborate during tactical mission planning and execution

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?” Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

Big Data in MultiAgent Systems: Market Design Solutions

El objetivo principal de esta Tesis es presentar un conjunto de novedosos y diferentes métodos en los que los sistemas multiagente pueden jugar un papel clave en predicciones y modelos económicos en un amplio conjunto de contextos. La hipótesis principal es que los sistemas multiagente permiten la creación de modelos macroeconómicos con microfundamentos reales que son capaces de representar la economía en los diferentes niveles de acuerdo con diferentes propósitos y necesidades. La investigación se estructura en seis capítulos. El Capítulo 1 es una introducción teórica al resto de los capítulos que presentan aplicaciones empíricas. En él se compara los sistemas multiagente con dos alternativas: los modelos de equilibrio general computable y la econometría espacial. El resto de los capítulos son intencionadamente diferentes en sus objetivos y sus contenidos. Estas cinco aplicaciones incorporan diferentes tipos de agentes: incluyen individuos (2, 5, 6), familias (2, 5), empresas (3, 5, 6), establecimientos (5), instituciones financieras (6) y usuarios (4). En el ámbito espacial, la desagregación espacial es deliberadamente diferente en cada aplicación: El capítulo 4 no incluye el espacio, El capítulo 6 es una aplicación para la zona euro en su conjunto y en el capítulo 3 se toma España en su conjunto. Los capítulos 2 y 5 exploran las dos de las principales posibilidades para la incorporación del espacio en los sistemas multiagente: el capítulo 2 incluye las regiones NUTS 3 de la Unión Europea y en el capítulo 5 se geolocalizan los agentes. En el capítulo 2 se desarrolla un sistema multiagente que incluye a todos los individuos de la Unión Europea. Con este sistema podemos predecir la población a escala regional para toda la Unión Europea y cómo distintos niveles de crecimiento económico repercuten asimismo sobre el empleo. En el capítulo 3 se presenta un modelo de simulación con los principales puntos de vista de la teoría de negocios para estudiar el crecimiento empresarial y la demografía empresarial en un modelo evolutivo estocástico. El modelo que se presenta también muestra cómo las empresas se adaptan a los cambios en las características deseadas del producto y el efecto de la crisis sobre estas dinámicas. El capítulo 4 discute el papel clave de los incentivos en la seguridad de los sistemas de información. Trabajos anteriores realizan este estudio utilizando un enfoque de teoría de juegos, pero el capítulo muestra que un modelo basado en agentes es capaz de incluir la heterogeneidad y las interrelaciones entre los individuos, y no se centra en el equilibrio alcanzado sino en la dinámica antes de su aparición. El objetivo del capítulo 5 es el estudio de los efectos de la Ley para la Revitalización Comercial (Ley de Dinamización Comercial) que fue aprobada en la Comunidad de Madrid durante el año 2012. Por último, el objetivo del capítulo 6 es explicar los determinantes de la inflación y pronosticar la tasa de inflación en la zona euro en los próximos cinco años. Se predice una inflación para la zona euro creciente hasta 2018 con un límite cercano al 2,5% en tasa interanual siempre que no se produzcan perturbaciones externas relevantes