Knowledge model reuse: therapy decision through specialisation of a generic decision model

Abstract

Abstract We present the definition of the therapy decision task and its associated Heuristic Multi-Attribute (HM) 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 (more specialised but still generic) 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.