Learning Abduction under Partial Observability
This work addresses a limitation in machine learning for abductive reasoning, making it more applicable to real-world scenarios with incomplete data, though it is incremental as it builds on an existing framework.
The paper tackles the problem of learning abductive reasoning from partially observable examples, extending a prior formulation that assumed full information, and shows that small, human-understandable explanations can lead to improved guarantees in exception-tolerant settings.
Juba recently proposed a formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. The main shortcoming of this formulation of the task is that it assumes access to full-information (i.e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information. In this work, we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting. Such small, human-understandable explanations are of particular interest for potential applications of the task.