Learning Abduction under Partial Observability

11/13/2017
by   Brendan Juba, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/27/2023

On Logic-Based Explainability with Partially Specified Inputs

In the practical deployment of machine learning (ML) models, missing dat...
research
09/01/2012

Learning implicitly in reasoning in PAC-Semantics

We consider the problem of answering queries about formulas of propositi...
research
05/29/2023

Reason to explain: Interactive contrastive explanations (REASONX)

Many high-performing machine learning models are not interpretable. As t...
research
06/29/2021

Semantic Reasoning from Model-Agnostic Explanations

With the wide adoption of black-box models, instance-based post hoc expl...
research
07/21/2023

Providing personalized Explanations: a Conversational Approach

The increasing applications of AI systems require personalized explanati...
research
03/27/2013

Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities

The multiple extension problem arises frequently in diagnostic and defau...
research
10/26/2020

Learning in Order: Steps of Acquiring the Concept of the Day/Night Cycle

This chapter presents a detailed model of children's explanations of whe...

Please sign up or login with your details

Forgot password? Click here to reset