Several explanation and interpretation tasks, such as diagnosis, plan recognition and image interpretation, can be formalized as abductive reasoning. A number of approaches, including recent ones, address the problem based on a task-independent representation of a domain which includes an ontology or taxonomy of hypotheses. In this paper we adopt a similar representation, but we also deal with abduction as an iterative process where, like in model-based diagnosis, further observations are proposed to discriminate among candidate explanations; in addition, we take into account costs of observations and actions. In fact, discrimination also involves refining hypotheses, but this is performed down to an appropriate level which depends on the cost of actions (e.g. repair actions or therapy) to be taken based on the results of abduction, and on the cost of additional observations, which should be balanced with the benefits, in terms of more suitable actions, of better discrimination.
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