Hypothesis Discrimination with Abstractions based on Observation and Action Costs

Several explanation and interpretation tasks, such as diagnosis, plan recognition and image interpretation, can be formalized as abductive and consistency reasoning. Some proposals address the problem based on a task-independent representation of a domain which includes an ontology or taxonomy of hypotheses. In this paper we rely on the same type of representation, and we address cost trade-offs in abduction intended as an iterative process where, like in model-based diagnosis, further observations are proposed to discriminate among candidates. Discrimination is performed up 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. Abstractions have a significant impact on this trade-off, given that the cost of observing the same phenomenon at different levels of abstraction may be quite different.