Problem-solving methods provide reusable architectures and components for implementing the reasoning part of knowledge-based systems.

This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed. 1. INTRODUCTION Among AI-ED research done to date, several paradigms such as CAI, ICAI, Micro-world, ITS, ILE, and CSCL have been proposed and many systems have been built within each paradigm. Additionally, innovative computer technologies such as hyper-media, virtual reality, internet, WWW have significantly affected the AI-ED community in general. We really have learned a lot ...

The proliferation of different standards and joint initiatives for the classification of products and services (UNSPSC, e-cl@ss, RosettaNet, NAICS, SCTG, etc.) reveals that B2B markets have not reached a consensus on coding systems, level of detail of their descriptions, granularity, etc. This paper shows how these standards and initiatives, which are built to cover different needs and functionalities, can be integrated using a common multi-layered knowledge architecture through ontological mappings. This multi-layered ontology will provide a shared understanding of the domain for applications of e-commerce, allowing information sharing and interoperation between heterogeneous systems. We will present a tool called WebPicker and a method for integrating these standards and initiatives, enriching them and obtaining the results in different formats using the WebODE platform. As an illustration, we show a case study on the computer domain, presenting the ontological mappings between UNSPSC, e-cl@ss, RosettaNet and an electronic catalogue from an e-commerce platform. 1

In recent years two main technologies for knowledge sharing and reuse have emerged: ontologies and problem solving methods (PSMs). Ontologies specify reusable conceptualizations which can be shared by multiple reasoning components communicating during a problem solving process. PSMs describe in a domain-independent way the generic reasoning steps and knowledge types needed to perform a task. Typically PSMs are specified in a task-specific fashion, using modelling frameworks which describe their control and inference structures as well as their knowledge requirements and competence. In this paper we discuss a novel approach to PSM specification, which is based on the use of formal ontologies. In particular our specifications abstract from control, data flow and other dynamic aspects of PSMs to focus on the logical theory associated with a PSM (method ontology). This approach concentrates on the competence and knowledge requirements of a PSM, rather than internal control de...

Ontologies and problem-solving methods are promising candidates for reuse in Knowledge Engineering. Ontologies define domain knowledge at a generic level, while problem-solving methods specify generic reasoning knowledge. Both type of components can be viewed as complementary entities that can be used to configure new knowledge systems from existing, reusable components. In this paper, we give an overview of approaches for ontologies and problem-solving methods. 1 Introduction In 1991, the ARPA Knowledge Sharing Effort [NFF 91] envisioned a new way in which intelligent systems could be built. They proposed the following: "Building knowledgebased systems today usually entails constructing new knowledge bases from scratch. It could be done by assembling reusable components. Systems developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, us...

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 work reported in this thesis is motivated by the belief that knowledge-based systems (KBS) research needs to focus more on users ’ needs and cater for the various decision situations in which users will find themselves. To build individual systems that cater for all the activities that may be needed is not feasible or desirable. The problems associated with capturing knowledge are well known and the ability to capture knowledge once and access and manipulate the knowledge in multiple ways is highly desirable. It adds value to the original knowledge and offers all the benefits associated with the reuse of resources. Thus, the problem becomes one of knowledge reuse. The research question pursued in this thesis is “can knowledge captured for one purpose, such as consultation, be reused to support a wide range of alternative purposes, such as critquing or tutoring, allowing the user to answer different types of questions according to their current circumstances”? Further, this question was to be answered in a situated cognition, dynamic knowledge framework. The system developed in this thesis is based on the Multiple Classification Ripple Down Rule (MCRDR) knowledge acquisition and representation technique. MCRDR is a form of casedbased

INTRODUCTION In 1991, the ARPA Knowledge Sharing Eort (Neches et al., 91) revolutionized the way in which intelligent systems were built. They proposed the following: \Building Knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could be done by assembling reusable components. Systems developers would then only need to worry about creating the specialized knowledge and reasoners new to the specic task of their system, using them to perform some of its reasoning. In this way, declarative knowledge, problem-solving techniques and reasoning services would all be shared among systems. This approach would facilitate building bigger and better systems cheaply..." Since them, considerable progress has been made in developing the conceptual bases for building technology that allows knowledge-component reuse and sharing. To enable sharing and reuse of knowledge and reasoning behavior across domains and tasks, Ontologies and Problem Solving

. Developing software by selecting, adapting, combining, and integrating existing components instead of starting the system development process from scratch has become a key factor in economic software development. However, such a development process has to deal with four problems: First, components must be selected. Second, components must be adapted because they neither fit precisely the task that should be performed nor do they necessarily fit well to other selected components. Third, components must be combined and their interaction must be established. Fourth, it may be necessary to decompose complex problems into smaller subtasks for which components can be found. In this case, a general system frame must be established that enables to form an integrated system out of separate components. In this paper, we present our means to deal with these problems: brokers, adapters, connectors, and task structures. Although we discuss our approach in the context of problem-solving methods, s...

Ontological engineering as a key technology of the next generation knowledge processing is discussed. After a brief introduction to ontological engineering with my speculation about its potential contribution, three major results of the practice of ontological engineering in my lab are presented. Then, paradigm shift in information processing is discussed followed by a future directions in the Web intelligence context.