. This article is a preliminary survey of some methods, techniques and tools aimed at managing corporate knowledge from a corporate memory (CM) designer's perspective. In particular, it analyzes problems and solutions related to the following steps: detection of needs of CM, construction of the CM, its diffusion (specially using the Internet technologies), its use, its evaluation and its evolution. 1 INTRODUCTION 1.1 Corporate Memory: Definitions The objectives of knowledge management (KM) in an organization are to promote knowledge growth, knowledge communication and knowledge preservation in the organization (Steels, 93). Knowledge management is a very complex problem and can be tackled from several viewpoints: socio-organizational, financial and economical, technical, human, and legal (Barths, 1996). There is an increasing industrial interest in the capitalization of knowledge (i.e. both theoretical knowledge and practical know-how) of groups of people in an organization, such g...

Early work on the NONLIN and O-Plan projects indicated a need for a defined methodology which would guide users performing various roles in the acquisition and analysis of domain requirements for planning. This work included links to a requirement analysis methodology, CORE (COntrolled Requirements Expression) , tool support via an intelligent assistant as part of the Task Formalism (TF) Workstation and an initial collection of guidelines and checklists to aid in using the TF domain description language. This paper describes work underway to follow-on from this past research and to infuse it with knowledge gained from recent research related to planning domain development, knowledge modelling, design rationale and ontological and requirements engineering. Introduction The activities involved in discovering, engineering, documenting, and maintaining a set of domain constructs for most AI planning-based projects can be considered ad hoc and disorganised, at best. The current sources for...

The paper presents a case study that compares two of the existing knowledge modeling platforms: PROTG-II and KSM. These two software environments allow a developer to build a knowledge level model and to create the final operational version. Both environments have been used to develop real world models (e.g., for medical and traffic domains respectively). In the paper, we first describe a knowledge model following the KADS methodology that has been defined to carry out real-time decision support tasks in domains such as traffic control. Then, we present how the model is defined and operationalized using, first, the PROTÉGÉ-II environment and, second, the KSM environment. Finally, we discuss similarities and differences between both approaches.

Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.

Role-limiting approaches using explicit theories of problem-solving have been successful for acquiring knowledge from domain experts 1 .Howevermost systems using this approach do not support acquiring procedural knowledge, only instance and type information. Approaches using interdependencies among different pieces of knowledge have been successful for acquiring procedural knowledge, but these approaches usually do not provide all the support that domain experts require. We show how the two approaches can be combined in such a way that each benefits from information provided by the other. We extend the role-limiting approach with a knowledge acquisition tool that dynamically generates questions for the user based on the problem solving method. This allows a more flexible interaction pattern. When users add knowledge, this tool generates expectations for the procedural knowledge that is to be added. When these procedures are refined, new expectations are created from interdependency models that in turn refine the information used by the system. The implemented KA tool provides broader support than previously implemented systems. Preliminary evaluations in a travel planning domain show that users who are not programmers can, with little training, specify executable procedural knowledge to customize an intelligent system.

Computing increasingly addresses collaboration; sharing; and interaction involving distributed resources. This has been fuelled in part by the emergence of Grid technologies and web services. Drawing on our expertise in the Geodise project 1. We argue that there is a growing requirement for knowledge engineering methods that provide a semantic foundation for such distributed computing. Such methods also support the sharing and coordinated use of knowledge itself. In this paper we introduce a service-oriented knowledge engineering approach that seeks to provide knowledge orientated support for distributed grid-based computing. This approach has been implemented in a generic integrated architecture. The application context is the process of design search and optimisation in engineering. It demonstrates how knowledge has been captured and modelled, as well as illustrating how ontologies have been developed and deployed. The knowledge acquired has been made available and accessible through a portal that invokes a number of basic services.

This paper describes a design space analysis approach towards a "complete" planning solution. A complete solution is defined as one containing the resultant plan, the context in which it applies, and the argument structure that justifies it. The focus in this paper is on defining and communicating the argument structure component. A perspective of a plan as a specialised type of design and planning as a specialised form of design activity is used. In doing so, research is drawn upon from the design rationale community for generating an explanation of a designed artifact. In particular, the method of generating a design space which represents the location of the plan within the space of possible plan elaborations is adopted. An initial implementation, Nonlin+DR, is described and its potential benefits to stand-alone and mixed-initiative planning is discussed. Introduction The traditional solution produced by an artificial intelligence planning system is a set of actions and ordering co...

The lack of specialized professionals in network management and the growing complexity of this task has been aiming the need for developing tools to give support to the network administrator task. The construction of such tools requires an intense process of knowledge acquisition from experts in the area as well as the use of Artificial Intelligence (AI) techniques. A number of different approaches have been proposed, evolving from rule-based systems through case-based systems, to more recent modelbased systems [6] [7] [8] [9] [11]. A special attention has been given to propose systems to solve two main network management tasks: the fault diagnosis and performance management. The aim of this paper is to specify a Communication Fault Diagnostic System applying the AI Model Based approach.. We claim that this approach provides a foundation for exchanging behavioral, structural and control information between the sub-tasks of such complex systems. We also show what are the main aspects to be considered when constructing such systems: the construction of an automatic network discovery system and a configuration diagnosis system, both to support the construction of the network configuration model, and a network status gathering system to allow the diagnosis system to observe the network. 1.

Proper education of a modeling and simulation professional meeting the extensive criteria imposed by the community poses significant challenges. In this paper, we explore the formation of a university-based education in modeling and simulation to meet the challenges. We examine the factors affecting the composition of a modeling and simulation course. Based on the anticipated consequences, we propose potential solutions.