Learning Abduction under Partial Observability

TL;DR

This paper extends learning abductive reasoning to partially observed data, integrating declarative background knowledge to improve hypothesis generation, especially for small, understandable explanations in complex scenarios.

Contribution

It introduces a framework for abductive learning with partial examples and background knowledge, enabling implicit rule learning and improved guarantees for small explanations.

Findings

Enhanced abduction with partial data and background knowledge

Ability to generate small, human-understandable explanations

Improved guarantees in exception-tolerant settings

Abstract

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.