document: Knowledge model reuse : therapy decision through specialisation of a generic decision model We present the definition of the therapy decision task and its associated Heuristic Multi-Attribute -LRB- HM -RRB- solving method , in the form of a KADS-style specification . The goal of the therapy decision task is to identify the ideal therapy , for a given patient , in accordance with a set of objectives of a diverse nature constituting a global therapy-evaluation framework in which considerations such as patient preferences and quality-of-life results are integrated . We give a high-level overview of this task as a specialisation of the generic decision task , and additional decomposition methods for the subtasks involved . These subtasks possess some reflective capabilities for reasoning about self-models , particularly the learning subtask , which incrementally corrects and refines the model used to assess the effects of the therapies . This work illustrates the process of reuse in the framework of AI software development methodologies such as KADS-CommonKADS in order to obtain new -LRB- more specialised but still generic -RRB- components for the analysis libraries developed in this context . In order to maximise reuse benefits , where possible , the therapy decision task and HM method have been defined in terms of regular components from the earlier-mentioned libraries . To emphasise the importance of using a rigorous approach to the modelling of domain and method ontologies , we make extensive use of the semi-formal object-oriented analysis notation UML , together with its associated constraint language OCL , to illustrate the ontology of the decision method and the corresponding specific one of the therapy decision domain , the latter being a refinement via inheritance of the former
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