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 GALEN prALogramme has been developing medical ontologies collaboratively for nearly a decade. The ontologies are large and formulated in a specialised description logic, GRAIL. The programme is a broad collaboration of over a dozen groups, most with no prior experience of developing formal ontologies. The programme has developed a methodology for loosely coupled development using layers of intermediate representations, guidelines and tools which minimises training requirements for domain experts and effort by central knowledge engineers. Issues arise both from problems in formal representations and from the idiosyncrasies of the medical domain. Issues dealt with include ‘tangled ’ taxonomies, part-whole and locative relationships, defaults and exceptions, semantic normalisation, and the difference between medical convention and strict logical criteria for correctness. Keywords: Cooperative development; ontology development; ontology design; very large ontologies, medical

The CommonKADS methodology is a collection of structured methods for building knowledge based systems. Akey component ofCommonKADS is the library of generic inference models which can be applied to tasks of speci ed types. These generic models can either be used as frameworks for knowledge acquisition, or to verify the completeness of models developed by analysis of the domain. However, the generic models for some task types, such asknowledge-based planning, are not welldeveloped. Since knowledge-based planning is an important commercial application of Arti cial Intelligence, there is a clear need for the development of generic models for planning tasks. Many of the generic models which currently exist have been derived from modelling of existing AI systems. These models have the strength of proven applicability. There are a number of well-known and well-tried AI planning systems in existence� one of the best known is the Open Planning Architecture (O-Plan). This paper describes the development ofaCommonKADS generic inference model for knowledgebased planning tasks, based on the capabilities of the O-Plan system. The paper also brie y describes the veri cation of this model in the context of a real-life planning task: the assignment and management of RAF Search and Rescue operations. 2

The KADS methodology [Schreiber et al, 1993] [Tansley & Hayball, 1993] and its successor, CommonKADS [Wielinga et al, 1992]) have proved to be very useful approaches for modelling the various transformations involved between eliciting knowledge from an expert and encoding this knowledge in a computer program. These transformations are represented in a series of models. While it is widely agreed that these methods are excellent approaches from a theoretical viewpoint, the documentation provided concentrates on defining what models should be produced, with only general guidance on how the models should be produced. This has the advantage of making KADS and CommonKADS widely applicable, but it also means that considerable training and experience is required to become proficient in them. This paper reviews three projects, which investigated the feasibility of producing specific guidance for certain decisions which are required when using KADS or CommonKADS to develop a knowled...

Nowadays, technology-enhanced learning systems must have the ability to reuse learning resources from distributed repositories, to take into account the context and to allow dynamic adaptation to different learners (individuals, dyad or team) based on substantial advances in pedagogical theories and knowledge models. We focus on learning systems using a problem-based learning approach represented by scenarios. In our framework, the goal of scenarios is to describe the learning and tutoring activities to acquire some knowledge domain (for instance physics) and know-how to solve a particular problem. The main issue is to design a generic scenario which can deal with most of learning situation for problem-based learning science curriculum. From a generic scenario, the learning system will compute on the fly a particular scenario dedicated to the current learner and its learning situation. The main contribution of this paper is a semantic and didactic-based model of scenarios for designing an adaptive and context-aware learning System. The scenario model is acquired from: i) the know-how and real practices of teachers ii) the theory in didactic anthropology of knowledge of Chevallard [1]; iii) a hierarchical task model. 1.

ions In many domains, human experts employ data abstraction as a technique for reducing a large data set. Data abstraction is a powerful method that limits the search space and also reduces the size of the differential. Introducing finding abstraction in the generate-and-test model requires the specification of one additional function abstract which takes as input a set of findings and produces a new, more abstract, finding (see Fig. 8.6). From the taskknowledge point of view, abstraction typically has a recursive structure. An abstracted finding can be the input for another invocation of the abstraction knowledge source. finding hypothesis observable set of observables associate specify-1 obtain select-1 conjectured finding specify-2 solution select-2 finding select-3 finding abstract new hypotheis test observable new evidence abstract finding focus trigger specific finding FIGURE 8.6: Introducing finding abstractions Clancey describes three types of abst...

Adaptive and context-aware scenarios for technologyenhanced