Abstract. There is a strong argument in Artificial Intelligence in Education which advocates that computer-based learning systems need to adapt to the needs of learners if they are to provide for effective personalised instruction (Self, 1999a). Diagnosing a learner's cognitive capacity is a crucial issue in building adaptive systems. We have explored an interactive open learner modelling (IOLM) approach which conceives diagnosis as an interactive process involving both a computer system and a learner that discuss and together construct the learner model. This paper outlines the architecture of an interactive open learner modelling system and illustrates the method in a terminological domain. We discuss an evaluative study of an IOLM demonstrator – a system called STyLE-OLM. The results from the study demonstrate potential benefits of the method for improving the quality of the learner model and providing a means for fostering reflective thinking. We argue that IOLM is a fruitful approach which may be employed in intelligent learning environments both for obtaining a better model of a learner’s cognitive state and engaging learners in reflective activities.

Concept mapping has a history of use in many disciplines as a formal or semi-formal diagramming technique. Concept maps have an abstract structure as typed hypergraphs, and computer support for concept mapping can associate visual attributes with node types to provide an attractive and consistent appearance. Computer support can also provide interactive interfaces allowing arbitrary actions to be associated with nodes such as hypermedia links to other maps and documents. This article describes a general concept mapping system that is open architecture for integration with other systems, scriptable to support arbitrary interactions and computations, and cutomizable to emulate many styles of map. The system supports collaborative development of concept maps across local area and wide area networks, and integrates with World-Wide Web in both client helper and server gateway roles. A number of applications are illustrated ranging through education, artificial intelligence, active documents...

. Collective entities and collective relations play an important role in natural language. In order to capture the full meaning of sentences like "The Beatles sing `Yesterday' ", a knowledge representation language should be able to express and reason about plural entities -- like "the Beatles" -- and their relationships -- like "sing" -- with any possible reading (cumulative, distributive or collective). In this paper a way of including collections and collective relations within a concept language, chosen as the formalism for representing the semantics of sentences, is presented. A twofold extension of the ALC concept language is investigated : (1) special relations introduce collective entities either out of their components or out of other collective entities, (2) plural quantifiers on collective relations specify their possible reading. The formal syntax and semantics of the concept language is given, together with a sound and complete algorithm to compute satisfiability and subs...

Context appears in Artificial Intelligence (AI) as a challenge for the coming years as shown by the various scientific events focusing on context held since 1995. However, context is already considered in other domains, such as Natural Language Processing, although through few aspects of context. We present in this paper a survey of the literature dealing directly and explicitly with context whatever the domain is. This permits us to have a clear view of the context in AI. One of the conclusions of this survey is to point out the existence of different types of context along knowledge representation, the mechanisms of reasoning on the knowledge, and the interaction of the computer system with humans.

The Frame-logic (F-logic) approach of [20] is suggested as an underlying framework for description languages. F-logic is shown to provide a full account for description languages, without losing the direct semantics and the descriptive nature. It can support such desirable features as high order role fillers, collective entities, intensions, roles as first class objects and n-ary relationships. Yet, its semantics is first order. In an F-logic based description language, few description constructs are built in, and concepts, roles, and terminological operators are definable. Discussion of desirable features in descriptions is made possible within a single, uniform framework, that also coherently integrates with logic programming and deductive, object-oriented database technology. Typical descriptive operators can be defined in the language, thereby yielding a flexible description language, in which not all operators must be built in. keywords: object-oriented representation, F-logic, de...

Conceptual graphs are very useful for representing structured knowledge. However, existing formulations of fuzzy conceptual graphs are not suitable for matching images of natural scenes. This paper presents a new variation of fuzzy conceptual graphs that is more suited to image matching. This variant differentiates between a model graph that describes a known scene and an image graph which describes an input image. A new measurement is defined to measure how well a model graph matches an image graph. A fuzzy graph matching algorithm is developed based on error-tolerant subgraph isomorphism.

The elicitation of the ontology -- i.e. the objects of a domain -- is a key issue of conceptual modelling and therefore of knowledge acquisition. The Conceptual Graph Theory provides a knowledge representation formalism to be used in knowledgebased systems with an explicit "type lattice" to account for the ontology. Since knowledge is in most AI applications non formal, it has to be normalized to ensure that the formal exploitation of its representation conforms to its meaning in the domain. Noting the intensional nature of types, which reflect the essences of the objects they denote, this normalization relies on a commitment on type definitions by necessary and sufficient conditions at the knowledge level. Our claim is that the taxonomic structure that accounts for the intensional nature of the ontology can be nothing but a tree, precluding tangled taxonomies. From this starting point, we derive methodological principles to constrain the acquisition of the "type tree", thus...

Over the ten years a community on context has emerged. Brzillon (1999) proposed a survey of the literature about context in artificial intelligence. There is a now series of conference on context, a web site and a mailing list. The number of web pages with the word "context" has been multiply by four in the last five years. Being among the instigators of the use of context in real-world applications, we present in this paper the evolution of our thoughts over the last years and the result that is obtained as a representation formalism based on contextual graphs and used in a real-world application called SART. We also point out some similarities between contextual graphs and some models developed in other domains.

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

Context appears in AI as a challenge for the coming years as shown by the various scientific events focusing on context held since 1995. However, context is already considered in other domains, as Natural Language, although through few aspects of context. We present in this paper a survey of the literature dealing directly and explicitly with context whatever the domain is. This permits to have a clear view on the context in AI. One of the conclusions of this survey is to point out the existence of different types of context along knowledge representation, the mechanisms of reasoning on the knowledge, and the interaction of the computer system with a human.