A multi-agent-based communication prototype for cross-company capacity exchange in manufacturing networks

Manufacturing companies face tough challenges in leveling their capacity load due to unsteady market conditions, like fluctuating demand. This induces frequent re-scheduling in order to harmonize the capacity utilization of machine tools to meet customer requirements in terms of delivery time, costs, and product quality. In most cases an internal adjustment is not possible, so that a subcontracting is unavoidable. This requires significant efforts and can be very time and cost intensive. So, there is a need for a system supporting the cross-company capacity exchange which is yet not addressed in both literature and industry. This paper deals with the development of such a capacity exchange system and presents first result of its application

An Actor-Oriented and Architecture-Driven Approach for Spatially Explicit Agent-Based Modeling

Nowadays, there is an increasing need to rapidly build more realistic models to solve environmental problems in an interdisciplinary context. In particular, agent-based and spatial modeling have proven to be useful for understanding land use and land cover change processes. Both approaches include simulation platforms often used in several research domains to develop models explaining and analyzing complex phenomena. Domain experts generally use an ad hoc approach for model development, which relies on a code-and-fix life cycle, going from a prototype model through progressive refinement. This adaptive approach does not capture systematically actors’ knowledge and their interactions with the environment. The development and maintenance of resulting models become cumbersome and time-consuming. In this article, we propose an actor and architecture-driven approach that relies on relevant existing methods and satisfies the needs of spatially explicit agent-based modeling and implementation. We have designed an Agent Global Experiment framework incorporating a meta-model built from actor, agent architecture, and spatial concepts to produce an initial model from specifications provided by domain experts and system analysts. An engine is built as a tool to support model transformation. Domain knowledge including spatial specifications is summarized in a class diagram which is later transformed into the agent-based model. Finally, the XML file representing the model produced is used as input in the transformation process leading to code. This approach is illustrated on a hunting and population dynamic model to generate a running code for GAMA, an agent-based and spatially explicit simulation platform

Agent Oriented Software Engineering (AOSE) Approach to Game Development Methodology

This thesis investigates existing game development methodologies, through the process of researching game and system development models. The results indicate that these methodologies are engineered to solve specific problems, and most are suitable only for specific game genres. Different approaches to building games have been proposed in recent years. However, most of these methodologies focus on the design and implementation phase. This research aims to enhance game development methodologies by proposing a novel game development methodology, with the ability to function in generic game genres, thereby guiding game developers and designers from the start of the game development phase to the end of the implementation and testing phase. On a positive note, aligning development practice with universal standards makes it far easier to incorporate extra team members at short notice. This increased the confidence when working in the same environment as super developers. In the gaming industry, most game development proceeds directly from game design to the implementation phase, and the researcher observes that this is the only industry in which this occurs. It is a consequence of the game industry’s failure to integrate with modern development techniques. The ultimate aim of this research to apply a new game development methodology using most game elements to enhance success. This development model will align with different game genres, and resolve the gap between industry and research area, so that game developers can focus on the important business of creating games. The primary aim of Agent Oriented Agile Base (AOAB) game development methodology is to present game development techniques in sequential steps to facilitate game creation and close the gap in the existing game development methodologies. Agent technology is used in complex domains such as e-commerce, health, manufacturing, games, etc. In this thesis we are interested in the game domain, which comprises a unique set of characteristics such as automata, collaboration etc. Our AOAB will be based on a predictive approach after adaptation of MaSE methodology, and an adaptive approach using Agile methodology. To ensure proof of concept, AOAB game development methodology will be evaluated against industry principles, providing an industry case study to create a driving test game, which was the problem motivating this research. Furthermore, we conducted two workshops to introduce our methodology to both academic and industry participants. Finally, we prepared an academic experiment to use AOAB in the academic sector. We have analyzed the feedbacks and comments and concluded the strengths and weakness of the AOAB methodology. The research achievements are summarized and proposals for future work outlined

An algebraic framework for compositional design of autonomous and adaptive multiagent systems

Doctor of PhilosophyDepartment of Computing and Information SciencesScott A. DeLoachOrganization-based Multiagent Systems (OMAS) have been viewed as an effective paradigm for addressing the design challenges posed by today’s complex systems. In those systems, the organizational perspective is the main abstraction, which provides a clear separation between agents and systems, allowing a reduction in the complexity of the overall system. To ease the development of OMAS, several methodologies have been proposed. Unfortunately, those methodologies typically require the designer to handle system complexity alone, which tends to lead to ad-hoc designs that are not scalable and are difficult to maintain. Moreover, designing organizations for large multiagent systems is a complex and time-consuming task; design models quickly become unwieldy and thus hard to develop. To cope with theses issues, a framework for organization-based multiagent system designs based on separation of concerns and composition principles is proposed. The framework uses category theory tools to construct a formal composition framework using core models from the Organization-based Multiagent Software Engineering (O-MASE) framework. I propose a formalization of these models that are then used to establish a reusable design approach for OMAS. This approach allows designers to design large multiagent organizations by reusing smaller composable organizations that are developed separately, thus providing them with a scalable approach for designing large and complex OMAS. In this dissertation, the process of formalizing and composing multiagent organizations is discussed. In addition, I propose a service-oriented approach for building autonomous, adaptive multiagent systems. Finally, as a proof of concept, I develop two real world examples from the domain of cooperative robotics and wireless sensor networks

Agent-based management of clinical guidelines

Les guies de pràctica clínica (GPC) contenen un conjunt d'accions i dades que ajuden a un metge a prendre decisions sobre el diagnòstic, tractament o qualsevol altre procediment a un pacient i sobre una determinada malaltia. És conegut que l'adopció d'aquestes guies en la vida diària pot millorar l'assistència mèdica als pacients, pel fet que s'estandarditzen les pràctiques. Sistemes computeritzats que utilitzen GPC poden constituir part de sistemes d'ajut a la presa de decisions més complexos amb la finalitat de proporcionar el coneixement adequat a la persona adequada, en un format correcte i en el moment precís. L'automatització de l'execució de les GPC és el primer pas per la seva implantació en els centres mèdics.Per aconseguir aquesta implantació final, hi ha diferents passos que cal solucionar com per exemple, l'adquisició i representació de les GPC, la seva verificació formal, i finalment la seva execució. Aquesta Tesi està dirigida en l'execució de GPC i proposa la implementació d'un sistema multi-agent. En aquest sistema els diferents actors dels centres mèdics coordinen les seves activitats seguint un pla global determinat per una GPC. Un dels principals problemes de qualsevol sistema que treballa en l'àmbit mèdic és el tractament del coneixement. En aquest cas s'han hagut de tractar termes mèdics i organitzatius, que s'ha resolt amb la implementació de diferents ontologies. La separació de la representació del coneixement del seu ús és intencionada i permet que el sistema d'execució de GPC sigui fàcilment adaptable a les circumstàncies concretes dels centres, on varien el personal i els recursos disponibles.En paral·lel a l'execució de GPC, el sistema proposat manega preferències del pacient per tal d'implementar serveis adaptats al pacient. En aquesta àrea concretament, a) s'han definit un conjunt de criteris, b) aquesta informació forma part del perfil de l'usuari i serveix per ordenar les propostes que el sistema li proposa, i c) un algoritme no supervisat d'aprenentatge permet adaptar les preferències del pacient segons triï.Finalment, algunes idees d'aquesta Tesi actualment s'estan aplicant en dos projectes de recerca. Per una banda, l'execució distribuïda de GPC, i per altra banda, la representació del coneixement mèdic i organitzatiu utilitzant ontologies.Clinical guidelines (CGs) contain a set of directions or principles to assist the health care practitioner with patient care decisions about appropriate diagnostic, therapeutic, or other clinical procedures for specific clinical circumstances. It is widely accepted that the adoption of guideline-execution engines in daily practice would improve the patient care, by standardising the care procedures. Guideline-based systems can constitute part of a knowledge-based decision support system in order to deliver the right knowledge to the right people in the right form at the right time. The automation of the guideline execution process is a basic step towards its widespread use in medical centres.To achieve this general goal, different topics should be tackled, such as the acquisition of clinical guidelines, its formal verification, and finally its execution. This dissertation focuses on the execution of CGs and proposes the implementation of an agent-based platform in which the actors involved in health care coordinate their activities to perform the complex task of guideline enactment. The management of medical and organizational knowledge, and the formal representation of the CGs, are two knowledge-related topics addressed in this dissertation and tackled through the design of several application ontologies. The separation of the knowledge from its use is fully intentioned, and allows the CG execution engine to be easily customisable to different medical centres with varying personnel and resources.In parallel with the execution of CGs, the system handles citizen's preferences and uses them to implement patient-centred services. With respect this issue, the following tasks have been developed: a) definition of the user's criteria, b) use of the patient's profile to rank the alternatives presented to him, c) implementation of an unsupervised learning method to adapt dynamically and automatically the user's profile.Finally, several ideas of this dissertation are being directly applied in two ongoing funded research projects, including the agent-based execution of CGs and the ontological management of medical and organizational knowledge

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

A platform-independent domain-specific modeling language for multiagent systems

Associated with the increasing acceptance of agent-based computing as a novel software engineering paradigm, recently a lot of research addresses the development of suitable techniques to support the agent-oriented software development. The state-of-the-art in agent-based software development is to (i) design the agent systems basing on an agent-based methodology and (ii) take the resulting design artifact as a base to manually implement the agent system using existing agent-oriented programming languages or general purpose languages like Java. Apart from failures made when manually transform an abstract specification into a concrete implementation, the gap between design and implementation may also result in the divergence of design and implementation. The framework discussed in this dissertation presents a platform-independent domain-specific modeling language for MASs called Dsml4MAS that allows modeling agent systems in a platform-independent and graphical manner. Apart from the abstract design, Dsml4MAS also allows to automatically (i) check the generated design artifacts against a formal semantic specification to guarantee the well-formedness of the design and (ii) translate the abstract specification into a concrete implementation. Taking both together, Dsml4MAS ensures that for any well-formed design, an associated implementation will be generated closing the gap between design and code.Aufgrund wachsender Akzeptanz von Agentensystemen zur Behandlung komplexer Problemstellungen wird der Schwerpunkt auf dem Gebiet der agentenorientierten Softwareentwicklung vor allem auf die Erforschung von geeignetem Entwicklungswerkzeugen gesetzt. Stand der Forschung ist es dabei das Agentendesign mittels einer Agentenmethodologie zu spezifizieren und die resultierenden Artefakte als Grundlage zur manuellen Programmierung zu verwenden. Fehler, die bei dieser manuellen Überführung entstehen, machen insbesondere das abstrakte Design weniger nützlich in Hinsicht auf die Nachhaltigkeit der entwickelten Softwareapplikation. Das in dieser Dissertation diskutierte Rahmenwerk erörtert eine plattformunabhängige domänenspezifische Modellierungssprache für Multiagentensysteme namens Dsml4MAS. Dsml4MAS erlaubt es Agentensysteme auf eine plattformunabhängige und graphische Art und Weise darzustellen. Die Modellierungssprache umfasst (i) eine abstrakte Syntax, die das Vokabular der Sprache definiert, (ii) eine konkrete Syntax, die die graphische Darstellung spezifiziert sowie (iii) eine formale Semantik, die dem Vokabular eine präzise Bedeutung gibt. Dsml4MAS ist Bestandteil einer (semi-automatischen) Methodologie, die es (i) erlaubt die abstrakte Spezifikation schrittweise bis hin zur konkreten Implementierung zu konkretisieren und (ii) die Interoperabilität zu alternativen Softwareparadigmen wie z.B. Dienstorientierte Architekturen zu gewährleisten

RODE: Learning Roles to Decompose Multi-Agent Tasks

Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents. Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces. We further integrate information about action effects into the role policies to boost learning efficiency and policy generalization. By virtue of these advances, our method (1) outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark and (2) achieves rapid transfer to new environments with three times the number of agents. Demonstrative videos are available at https://sites.google.com/view/rode-marl