A multi-agent architecture to synthesize industrial knowledge from a PLM system

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1 Original Article Proceedings of Virtual Concept 2006 Playa Del Carmen, Mexico, November 26 th December 1 st, 2006 A multi-agent architecture to synthesize industrial knowledge from a PLM system Davy Monticolo 1,2, Samuel Gomes 2, Vincent Hilaire 2, Patrick Serrafero 3 (1) : Zurfluh-Feller Company Roide FRANCE Davy.Monticolo@zurfluh-feller.com (2) : SeT laboratory, UTBM Belfort Cedex FRANCE {Samuel.Gomes,Vincent.Hilaire}@utbm.fr (3) : Ecole Centrale de Lyon Ecully Cedex, FRANCE Patrick.Serrafero@free.fr Abstract: This paper presents a knowledge management experiment carried out in an industrial company. Our research concerns the development of a knowledge engineering module integrated in a PLM system which is based on a multi-domain scheme (project, product, process and use) taking into consideration several viewpoints (structural, functional, dynamic, etc.). This PLM system enables us to capture technical data and information throughout design projects. The development of this PLM system concerns the implementation of knowledge engineering features, using multi-agent technology. This approach allows us to model the collaborative engineering activities and synthesize industrial knowledge during projects (vocabulary, rules, experiences, etc.) using technical information Key words: PLM, Knowledge Engineering, Collaborative Design, Multi Agents System 1- Introduction To survive in an increasingly competitive business environment, manufacturing enterprises are under unprecedented pressure to become leaner and more agile. They must bring innovative products to market more effectively and more quickly to maximize customer interest and sales. The pressure to reduce time, improve product quality, and lower costs has not gone away; it is being reaffirmed and incorporated into programs that focus on delivering the right product. To continue to expand, product-leader companies must continue to enter new markets with innovative products. This requires developing and reusing the product-related intellectual capital created by business partners working together across the extended enterprise value chain. Business innovation must occur in several dimensions: project organization, product definition, production engineering, ergonomic design, environmental impact, etc. Product Lifecycle Management (PLM) supports a strategic business approach with a consistent set of methodologies and software solutions for promoting the collaborative creation, management, distribution and proper use of product lifecycle definition information across the extended enterprise [HE], [SI] [ST] While Product Data Management becomes a reality in design departments, companies have now to differentiate themselves by extending their strategic business approach to the entire Product Lifecycle and by deploying Knowledge Management techniques [SH]. More than product information storage, they must now capture, manage and control their industrial knowledge in order to achieve their business goals, namely reducing costs, improving quality and shortening time to market. Recent works prove that the modeling of product lifecycle define a referential for professional Knowledge. Among those there are the building of Intelligent Products Manual (IPMl) [SE] where authors capture a collection of product data and knowledge, the Knowledge Based approximate Life Cycle Assessment System (KALCAS) [PA] using neural networks to identify product attributes and the Knowledge Intensive Engineering Framework (KIEF) [YO] defined an ontology form the product lifecycle. Our approach consists in associating a Knowledge Engineering package to a PLM System in order to identify, capitalize, synthesize and reuse technical knowledge. We use a web-based PLM system built from a collaborative and concurrent project/product/process/use engineering approach, which has been developed since 1996 [GS]. Based on these concepts, this web-based environment (named in French ACSP for "Atelier Coopératif de Suivi de Projets") enables the project team members to organize their cooperative design activities. Thus the results of these cooperative activities are stored in the ACSP framework. In association with a knowledge Engineering package managed by a multi agent system, we propose efficient capitalization, traceability, synthesis and reuse of technical knowledge. Our work is deployed in a company of four hundred employees in the field of rolling window shutters. The Paper Number -1- Copyright of Virtual Concept

2 research and development department counts fifty technicians. The method department, laboratories and the design and engineering departments work together using in a concurrent engineering project organization. One of the problems is to ensure that professional actors are able to reuse their collaborative professional experience from past projects. Thus the company management has decided to develop a knowledge engineering approach to resolve this problem. In the present paper, we present the PLM system ACSP which allows us to capture data and organize it to obtain information. We also present a project memory model to organize professional knowledge. Finally we present a Multi-Agents System architecture to extract and validate Knowledge from the PLM in order to build Project Memories in a semi automatic way. of the system. For example, applied to the product design domain, this kind of association generates functions commonly found in PDM (Product Data Management) and PLM (Product Lifecycle Management systems). More than a technology definition for product and part design, PLM can be defined as a business approach to solve managing problems of product definition information throughout its lifecycle. PLM is not just a technology, but is a strategic approach in which processes are as important, or more important than data. Project design domain Structural Functionnal Dynamic Usability design domain Structural Functionnal Dynamic 2- Knowledge Management Package linked to a PLM System Functionnal Functionnal 2.1 ACSP environment applying the multi-domain and multi-viewpoint systemic design model The model we present in this paper is based on the axiomatic design method developed by Suh [SU] taking into consideration design worlds which is divided into "design domains" and "aspects". This model is also based on systemic concepts and other design approaches such as integrated mechanical systems engineering methodology [TI] and distributed design theory [BR]. Situated in a connectionist paradigm, distributed design methodology can be described as a modular approach, where modules are connected in a network and where communication plays a major role, allowing the solution to emerge. Each module seeks to achieve its own local objective and needs its own tools. It is also necessary to exchange information between the different modules (interactions) in order to reach the solution. Practical experience in modeling has taught us that complex systems can be modeled and analyzed provided we adhere to certain sound principles such as modularity and abstraction. Systemic approaches adhere to these principles and provide a good support for building models that are closer to real-world complex systems. Based on these observations, it has been chosen to base our global design methodology on system theory within a concurrent engineering context. This methodology considers that a design project, in mechanical system engineering, is a network of various interacting design domains such as project, product, process, use, etc. (Figure 1). Each of these design domains can be examined from several viewpoints (or aspects) in interaction, as defined in the previous approach. We chose to develop three aspects in each design domain: a functional aspect, which describes the main objectives and goals of the system, a structural aspect, defining the system elements and architecture, a dynamic aspect, which describes the chronological behavior of the system. In this configuration, other design aspects such as physical or geometric models are directly linked to the structural aspect Structural Geometric Dynamic Product design domain Structural Dynamic Process design domain Figure 1: The design domain network. Functional, structural and dynamic viewpoints considered in each design domain The ACSP environment is a Web-based collaborative engineering environment, using the above described multidomain and multi-viewpoint design model and PLM concepts [ZS] This Web-based tool was developed in order to organize and structure the collaborative activities of designers from anywhere in the world [EG]. The ACSP software interface, connected to a relational Database Management System, is divided into four main sub-modules managing information from project, product, process and use design domains as presented in Figure 1. Each design domain includes various design data and files describing functional, structural and dynamic aspects of the domain studied. ACSP features are used for the design-chain data-management: product data and information, documents and their associated content (all types, formats and media), requirements (functional, performance, quality, cost, physical factors, interoperability, time, etc.), Product families, Product and project portfolios, plant machinery and facilities, production line equipment, etc. The two screenshots shown in Figure 2 and 3 illustrate Project and Product data examples extracted from a collaborative design project, developed through ACSP tool. These data management features are completed with various other engineering and management features: program and project management, 3D geometric visualization and collaboration, product and document definition information, drafting of technical publications such as user guides or assembly instructions. The next step consists in building a knowledge management methodology in order to extract knowledge from the information stored in the ACSP database. Paper Number -2- Copyright Virtual Concept

3 Figure 2: ACSP screenshot describing several tasks organized as a project plan Figure 3: ACSP screenshot describing a digital mock-up of a store rolling system 2.2 Knowledge engineering and project memory Knowledge engineering Knowledge Management is recognized as a cycle composed of socialization, explanation, organization and internalization of knowledge [NT]. Knowledge engineering defines methods and tools to model collective or individual knowledge [CH]. Rose Dieng-Kuntz defines the KM cycle as being composed of the following stages: clarification, broadcasting and reuse A solution to implement this sort of work is to define a corporate memory. A corporate memory is an explicit representation of the pertinent knowledge of an organization [DC]. This memory, which explains the organization knowledge, may be considered as a knowledge base of the organization. If we consider the organization as an engineering project, the corporate memory becomes a project memory. The project memory contains emanating Knowledge related to the engineering project. Moreover a complementary approach in knowledge engineering claims that knowledge is a personal interpretation of information. This theory is defended in several works which consist in searching for pertinent information instead of explanation. One can distinguish the works of M. Grundstein and JP. Barthès with Gameth [GR], S. Mahé with Puméo [MA]. For these authors, the knowledge is strongly dependant on a personal interpretation linked to a Paper Number -3- Copyright Virtual Concept

4 specific context. Without dealing with the context of this knowledge, Knowledge Management is meaningless. A different approach is the Knova theory [SE] which is based on the principle that we can measure the quantity of actual knowledge in the scientific and technical domain. To do that it defines a basic knowledge element : the cogniton. The theory is based on three assumptions: knowledge and information are different, knowledge has to be proven with experience, knowledge is measurable and its unit of measure is the kit (Knowledge digit). The gateway from information to knowledge" is defined by the equation: K = I T i.e. Cogniton (kit) = Information (bit) filtered by types and the symbol represent the filtering operation. This method aims to weigh (by means of the unit kits ) the appropriate knowledge to carry out an industrial product. The KNOVA theory is implemented in the KAD-Office software environment. It provides a knowledge broker (i.e. a set of tools which can capitalize on, arrange, restitute and maintain the engineering knowledge). This sequential and organizational knowledge management represents the knowhow of the company and enables a new innovation capacity to be achieved. Our approach is based upon these three theories. We use groupware which allows the clarification of the knowledge context and the actors involved. We attempt to build knowledge engineering tools which link the knowledge with its context in order to build knowledge traceability. One traceability process [DM] is to analyze interactions during meetings in order to: - Identify concepts, - Characterize psycho-cognitive, cooperative and sociological criteria, - Group those criteria to keep track of cooperative problems This traceability process allows the implicit knowledge emanating during meetings to be captured. This paper presents a different approach, with a traceability process to capture explicit knowledge. Indeed we limit our action to the storing of the knowledge contained in the results (schedule of condition, concepts, prototypes, etc) of the collaborative activities. Each store represents a knowledge trace which will be used to build the project memory Project Memory Carrying out a project in a company in a concurrent engineering context, requires that several teams from several companies and in several disciplines collaborate to carry out a design project. These teams are regarded as Co-partners who share their know-how during the project. If we don t save knowledge which has a collective dimension, produced during the the project, it is generally lost. Engineers will then begin a new project without reusing the collective experience of past projects. The documents produced in a project are not sufficient to keep track of knowledge, which even the head of project cannot explain. This dynamic character of knowledge is due to cooperative problem solving, where various ideas are confronted and which results in a cooperative definition of the solution produced. A project memory describes "the history of a project, the experience gained during the project" [MR] [BM]. The project memory contains knowledge regarding the context as well as the design rationale. Thus we have chosen to store this knowledge in the form of project memory. In project organization in a concurrent engineering context, project memories seem to be a good solution to capitalize on knowledge. Project memories save information about the progress of the project activities, from which we will be able to structure the types of knowledge to be filed [MH]. Knowledge related to the project development The product development lifecycle has the same phases (feasibility study, preliminary study, detailed study and industrialization) for each project. On the other hand the stages and sub-stages inside the phases can be different and be treated in various orders. The professional actors and particularly the project leader define the sequence of the stages for each phase. This sequence must be capitalized on, it presents the project progress and defines a system of reference to position the professional knowledge. To introduce the project progress into the project memory we present the project context i.e. its origins, its organization, its objectives, its participants, etc. Therefore, for knowledge related to the project progress, we obtain two elements of knowledge (Figure 4): The project context and the project development. The project context supplies all the knowledge characterizing the project categorized under three titles: Objectives, Environment and Organization. The project development makes it possible to describe all the project stages. This knowledge aggregate defines the system of reference for the knowledge capitalization. Figure 4: Knowledge related to the project progress Knowledge related to professional competences Competence is defined initially at the individual level: it is the capacity for an individual to implement his knowledge and to develop his know-how within a professional framework [LE]. In addition, collective competence is made by interaction with professional actors working together in the same department and in the same project team on a common undertaking [JK]. In order to capitalize on knowledge, we use the model Knova-Sigma [SE]. This model presents knowledge capitalization centred on human professional competences. We enrich this model by adding the knowledge element Paper Number -4- Copyright Virtual Concept

5 professional experience, composed of three types: successes, difficulties and failures (Figure 5). Professional Competence 1 Knowledge related of the professional competences Professional Competence 2 Professional Competence n Project Evolution Project Règle métier rules Project Expérience Experience métier Termes Project Terms métier Concerned roles Material Necessities elements Elements of Control Résults Stage Literal rule Formula Advise Choice Constraint Successes Difficulties failures Description Reason(s) Executed Actions Consequences Injunctions Definition Professional domain Example Comments Synonymous Representation Figure 5: Knowledge related to the professional competences Results of the use of project memories Today, in our company, knowledge of five finished projects is stored in project memories. Our first reports of the use of the projects memories allow us to draw up a table of some examples of use (Table 1). A test of project memory built directly by the professional actors shows us that this work is long, difficult and provides uncorrected results, if the latter are not submitted for a collective approval. Indeed the information capitalized on was mainly related to the professional actor s point of view. We have to define some referent professional actors, who are experts in specialties and validate Knowledge to validate Project Memories. Project Memory use To research the meaning and the representation of a technical element In the case of routine design, to find design parameters of a product element and design rules To reuse wrapping models for new parts To research solutions for quality problems To anticipate the project plan To choose a new project team To define a new industrialization process Knowledge element consulted Professional Terms Professional rules for the professional competence designing the product elements Professional rules for the professional competence developing and optimising the wrapping Professional experience for the professional competence : «injecting a plastic part» Project development Project context Professional process for the professional competence «industrializing the product elements» Table 1: Cases of Project Memory use Our experience in the company Zurfluh-Feller shows us that Knowledge books are difficult to write from interviews because this approach needs a great deal of human resource and time for each project. Moreover this work has to be carried out by a knowledge engineer to analyze each stage, to define knowledge and to capitalize on it. Nevertheless, during a project, engineers don t have time to answer interview questions and monitoring the design activity is difficult for the knowledge engineer to do. Consequently, we have to design and develop tools to assist engineers in making their know-how explicit, in order to facilitate the project memory construction. This requires understanding the collaborative design activity with the aim of defining knowledge emanating from it. 3- Modelling collaborative activities using the RIOCK formalism 3.1 From RIO to RIOCK Our specification approach uses an organizational model which is based on three interrelated concepts: Role, Interaction and Organization [HI]. Roles are generic behaviours. These behaviours can interact mutually according to interaction patterns. Such a pattern which groups generic behaviours and their interactions constitutes an organization. Organizations are thus descriptions of coordination structures. Coordination occurs between roles as and when interactions take place. In this context, an agent (human or software) is only specified as an active communicative entity which plays roles [FG]. Indeed agents instantiate an organization (roles and interactions) when they exhibit behaviours, defined by the organization s roles and when they interact, following the organization interactions. An agent may instantiate one or more roles and a role may be instantiated by one or more agents. We think this model is a basis for the engineering of societies of agents and what Castelfranchi calls social order [CA]. Paper Number -5- Copyright Virtual Concept

6 Figure 6. The RIOCK formalism In a social order, human agents have the capacity to implement their knowledge and to develop their know-how inside the professional framework. This capacity is called competence. Each competence is built with knowledge which is used to achieve a task. In addition Von Krogh and Roos [VR] explain that this knowledge can be transmitted and recognized only by interaction. By taking account of these assumptions, we propose to expand the RIO formalism to RIOCK (Role Interaction Organization Competence and Knowledge). We can thus define an organization where several roles are in interaction. Each role embodies one or several competences and each competence is aggregated by one or several competences (figure 6). The interaction between roles represents the collaborative work where each role used its competence within associated knowledge to satisfy the organization. 3.2 Using RIOCK to model the collaborative design activities The analysis of the activities over several projects in a company allows us to confirm the product development lifecycle with the four phases: feasibility study, preliminary study (called in the company pre-study), detailed study (named study) and industrialization. Each phase is structured according to some recurring usual stages for all projects. Those stages can be processed simultaneously or sequentially if the stage is a stake. At the time of the activity modeling, we consider a stage of the product development lifecycle as a RIOCK organization. For each stage, we define several roles according to the professional actors. We attribute to the latter the competences they use to tackle the stage. Each competence is described with a series of knowledge. The interaction between those roles highlights two types of results; exchanges between roles and emergence of knowledge. Thus, in a organization, a role uses one or more competences which require one or more pieces of knowledge. A role interacts with other roles to achieve a task and thus develop the collaborative work and create its result. In the stage, decision to launch the project in the feasibility study phase, we observe five roles (Figure 8). The role 'Commercial' uses two of its competences for this stage: To define the strategy for each customer and to know the functional needs for the customers. The first competence requires the knowledge of the overall marketing strategy of the company as well as the knowledge of the marketing strategy of the company towards the customer. The interaction among the five roles at this stage produces two results: the agreement for the launching of the project and the directives for the preliminary study. In modeling project engineering activities, we identify for every organization the knowledge emanating from it. Knowledge can be organized according to the types of knowledge defined by the project memory model presented previously. Indeed the modeling proves that the product lifecycle constitutes a system of reference split in two parts (figure 7), the project organization with phases, stages, roles and professional actors and a cognitive organization with roles, competences and Knowledge. We observe that roles are at the heart of these two worlds and bring every types of knowledge. Figure 7: Roles bring Knowledge In using RIOCK to analyze collaborative activities we deduce that roles bring every type of Knowledge defined in the project memory model (figure 8). Roles take part in the project organization (phases and tasks) and in the project social organization (professional actors and their Paper Number -6- Copyright Virtual Concept

7 competences). Indeed they build the cognitive organization by carrying every type of Knowledge. Figure 8: RIO model for the stage decision to launch the project 4- Traceability of knowledge leads by Agents 4.1 Why use the MAS paradigm A multi-agent system is composed of a number of agents which interact with one another. To successfully interact, agents will require the ability to cooperate, coordinate, and negotiate with each other, much as people do. Wooldridge and Jennings [WJ] list the following qualities of an agent: - Autonomy. Agents should operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state. - Social ability. Agents need to be able to interact with other agents (and possibly humans) via some kind of agent-communication language. - Reactivity. Agents should be able to perceive their environment and respond in a timely fashion to changes that occur in it. This environment may be the physical world, a user via a graphical user interface, a collection of other agents, the Internet, or perhaps all of these combined. - Pro-activeness. Agents should not simply act in response to their environment; they should be able to exhibit goal-directed behaviors by taking the initiative. By enumerating these qualities of agents we can easily show a similarity with an engineering project environment. Likewise, inside this distributed environment, we have professional actors, who will achieve the same goal. Each professional actor works individually and collaboratively to carry out engineering tasks. In this way MAS brings the cognitive and social approach into modelling the intelligent collective and individual behaviours [17]. 4.2 MAS architecture to trace Knowledge from roles We have seen previously with the RIOCK formalism, that roles carry competences and knowledge. With regard to this hypothesis, we have conceived an architecture called KATRAS (Knowledge Acquisition Traceability Reused Agents System) in three levels: - The first level ensures the communication with users inside the PLM platform. In this level we find the agents communities called Professional Agents (i.e. PA). Agents interpret the roles met during projects. Their objective is to ensure a traceability of the collaborative actions carried out by professional actors in order to capture emergent knowledge. There is one community PA per project - The second level gathers mechanisms of Knowledge capitalization. In this level we find communities of Knowledge Inductive Agents (i.e. KIA). The aim of KIA is to capitalize on knowledge traces of engineering activities communicated by the PA. The capitalization is done according to the project memory model presented in section 2. KIA exist for each project. - The third level contains the community of Knowledge deductive Agent (i.e. KDA). There is only one community KDA in which agents aim to synthesize the Knowledge structured according to the project memories for whole projects. Paper Number -7- Copyright Virtual Concept

8 4.2.1 Professional Agents (PA) community Behind this community, agents interpret roles of professional actors defined by the social model. These agents have two main functions: - Following collaborative actions in monitoring the PLM database, - Interacting with engineers to confirm the knowledge traces (Knowledge Project validation process). Professional Agents are created as soon as professional actors are assigned in a project. There is one agent for each professional actor per project. PA agents build some Knowledge Project traces based on deposited documents and data in the PLM database, which has been issued from engineering collaborative activities (figure 9). Each PA agent can also have the role of `Professional Referent' equally defined in the social organization. This role consists in submitting Knowledge traced by each PA Agent to the professional referent actor. Thus, the professional referent actor is assisted by its PA agent to confirm the knowledge before storage in the memory of a project. The PA agent manages a knowledge confirmation Interface. After validation, Knowledge is communicated to the Knowledge Inductive Agent community. Figure 9 KATRAS functional architecture Paper Number -8- Copyright Virtual Concept

9 Figure 10: KATRAS Multi Agents architecture Knowledge Inductive Agents As we saw previously (figure 10) Knowledge Inductive Agents (KIA) receive knowledge to be input by PA agents. This knowledge is sent in XML sequences (figure9). There are six KIA agents per project. They correspond to the six knowledge elements presented in the project memory model Project Context, Project Evolution, Project Experience, Project Process, Project Rules and Project Terms). Their roles are to structure received knowledge in order to build the project memory. Knowledge is organized according to an XML grammar defined in the same way as the project memory architecture. From this knowledge-base the KIA agents build a project memory in a Web format readable in the knowledge engineering module. The project memory is presented as knowledge, but slipping in the same provision, that the knowledge confirmation is done by the professional referents (firgure8). Another KIA agent s task is to transform knowledge in XML format to PDF format (figure 9) in order that professional actors can print it, enrich their knowledge books and consult the project memory on paper Knowledge Deductive Agents There is only one Knowledge Deductive Agent (KDA) community for all projects. In this community there are six agents which represent the six elements of the project memory model (figures 9). The roles of the KDA agents are to carry out a knowledge synthesis. Each KDA agent researches all established project memories to synthesize, process and bring reusable knowledge. For example, at the time of a new project creation, the KDA agent Project development proposes a project structure. In design engineering, projects belong to three categories (small projects, large projects and product development projects). The KDA Development Project agent seeks in all project memories the project courses for the category of corresponding project. From knowledge synthesized it is able to propose a project model with stages and meetings which are usually planned. Moreover it proposes dates for a pre-planning. From this model, the project leader chooses phases, stages and meetings to be planned and adjusts their due date. 4.3 Results and possibilities The KATRAS architecture implementation is carried out by using the Madkit platform Agent. At the present time communities of PA and AIK agents are operational. In the PA community the roles of project leader, project engineer, technical sales assistant and professional referent are implemented. For the AIK community all of the agents corresponding to the six knowledge elements of the project memory model are operational. These agents receive knowledge and are able to build project memories in XML format and to transform this knowledge using XSL-FO to provide a project memory in PDF format. This first development allows us: - To capitalize on knowledge from the design activity traces carried out by the roles of project leader, engineer, manager, technical sales assistant - To build project memories from this knowledge submitted between the two communities of agents, - To propose to the professional actors consultation of these memories either in pdf format or web format. 5- Conclusion The design of a product is a multi-skilled project where engineers with different specialities collaborate to achieve the same goal. These professional actors carry out tasks defined in the product lifecycle. Each stage requires the contribution of know-how and knowledge in order to achieve the goals laid down. In using a PLM platform with a multidomain and multi-viewpoint (Project, Product, Process), Paper Number -9- Copyright Virtual Concept

10 professional Engineers save Information related to the whole of the project. Our approach consists in analysing roles played by professional actors in order to define emanating knowledge in order to capitalize on it. Thus, we proposed a Knowledge Engineering Multi-Agent System which traces the engineering activities from professional actor s roles when using a PLM platform. From these activities our agents communities (PA agents) identify knowledge and interact with professional referent actors to confirm it. The next step consists in compiling project memories and make them consultable for the professional actors (PA agents). The first results of an assisted creation of Knowledge during projects shows that a multi agents system is suited to helping engineers. To ensure a high level of capitalization, agents have to understand the design process and the results of the collaborative activities. Our next task will be to equip our agents (KDA) with some inference rules in order to provide the pertinent Knowledge to design engineers. References [BM] Bekhti S., Matta N., Project memory: An approach of modelling and reusing the context and the design rationale, Proceedings of IJCAI'03 (International joint of conferences of Artificial Intelligence) Workshop on knowledge management and organisational memory, Accapulco, 2003 [BR] Brissaud D., Garro O., (1998) `Conception distribuée, émergence', Conception de produits mécaniques, méthodes, modèles et outils, sous la direction de M. Tollenaere, Editions Hermès, p [CA] Castelfranchi Cristiano. Engineering Social Order. In Engineering Societies in the Agents World, Lecture Notes in Artificial Intelligence. Springer Verlag, p. [CH] Charlet J., L ingénierie des connaissances, développements, résultats et perspectives pour la gestion des connaissances médicales, Mémoire d Habilitation à Diriger des Recherches, Université Pierre et Marie Curie, 2003 [DC] Dieng-Kuntz-Kuntz R., Corby O., Gandon F., Giboin A., Golebiowska J., Matta N., Ribière M., Méthodes et outils pour la gestion des connaissances. 2eme edition. Dunod éditeur [DM] Djaiz C., Monticolo D., Matta N., Project memory decision making, International Conference on Knowledge, Intelligent, and Creativity Support Systems, 2006 [EG] Eynard B., Gomes S. (2004) `Collaborative and remote design of mechatronic products'. In Perspective from Europe and Asia on Engineering, Design and Manufacture, Kluwer Academics Publishers, p [FG] Ferber, Gutknecht, A meta-model for the analysis and design of organizations in multi-agent systems. IEEE Computer Society, Proc. 3rd ICMAS, , [GS] Gomes S., Sagot J.C.. A concurrent engineering experience based on a cooperative and object oriented design methodology, 3rd International Conference on Integrated Design and Manufacturing in Mechanical Engineering, IDMME 2000, Montréal, May 16-19, [GR] Grundstein M., From capitalizing on Company Knowledge to Knowledge Management, chapter 12, pp , in Knowledge Management, Classic and Contemporary Works, Morey D., Maybury M., Thuraisingham B. (Eds), The MIT Press, Cambridge, Massachusetts, [HE] Helms R. W., (2002) `Product Data Management as enabler for concurrent engineering Controlling the flow of preliminary information in product development', PhD Thesis of Technische Universiteit Eindhoven, ISBN , 188 p. [HI] V. Hilaire, A. Koukam, P. Gruer, J-P. Müller, Formal specification and prototyping of multi-agent systems. In: Engineering Societies in the Agents World, n 1972, Lecture Notes in Artificial Intelligence, A. Omicini, R. Tolksdorf & F. Zambonelli (eds), Springer Verlag, [JK] Jyrki J.J, Kasvi, M. Vartiainen, M. Hailikari. Managing knowledge and knowledge competences in projects and project organisations, international journal of project management, oct 2002 [LE] Le Bortef G.. L ingénierie des compétences, éditions d organisation, octobre 2002 [MA] Mahé, Sylvain Démarche et outil actif de Gestion des Connaissances pour les P.M.I./P.M.E. Réutilisations et échanges de connaissances tacites, Rapport de thèse de l'université de Savoie, [MR] Matta N., Ribiere M., Corby O., Lewkowicz M., Zaclad. M. Project Memory in Design, Industrial Knowledge Management - A Micro Level Approach, Rajkumar Roy (Eds), Springer-Verlag, 2000 [MH] Monticolo D., Hilaire V., Koukam A., Meunier S., An approach for building Project Memories to facilitate design process in a concurrent engineering context, International Conference of Concurrent Engineering, Antibes 2006 [NT] Nonaka I., Takeushi H. (1995), The knowledge creating company: How Japanese Companies Create the Dynamics Innovation. Oxford University Press, Oxford, UK. [PA] Park Ji-Hyung, Seo Kwang-Kyu, A knowledge-based approximate life cycle assessment system for evaluating environmental impacts of product design alternatives in a collaborative design environment, Advanced Engineering Informatics, September 2006 [SI] Saaksvuori A., Immonen A. (2003) Product Lifecycle Management, Springer. [SE] Serrafero P., Cycle de vie, maturité et dynamique de la connaissance : des informations aux cognitons de l Entreprise Apprenante, revue ENSAM, décembre 2002 [SE1] Setchi R.M., Pham D.T., Dimov S.S. / A methodology for developing intelligent product manuals, Engineering Applications of Artificial Intelligence 19 (2006) [SH] Shen W. (2003), Knowledge Sharing in Collaborative Design Environments, Computers in Industry, Vol. 52, No. 1, pp [ST] Stark J. (2004) Product Lifecycle Management Paradigm for 21st century Product Realisation, Springer. [SU] Suh N.P., (1999) `Applications of Axiomatic Design', Integration of process Knowledge into Design Support, ISBN , Kluwer Academic Publishers. [TI] Tichkiewitch S., (1996) `Specifications on integrated design methodology using a multi-view product model', System Design and Analysis Conference, Montpellier, pp [VR] Von Krogh G. and J. Roos (1995), Conversation Management, European Mangament Journal, vol.13 [WJ] Wooldridge, Jennings, Kinny, The Gaia Methodology for Agent-Oriented Analysis and Design, Autonomous Agents and Multi-Agent Systems, 2000, 3(3): [Y0] Yoshioka M., Umeda Y., Takeda H., Shimomura Y., Physical concept ontology for the knowledge intensive engineering framework, Advanced Engineering Informatics, 2004 [ZS] Zhang S., Shen W., & Gheniwa H., (2003) `A review of Internet-based product information sharing and visualization', Computer in Industry. Paper Number -10- Copyright Virtual Concept