On the Generation of Alternative Explanations with Implications for Belief Revision

03/20/2013
by   Eugene Santos Jr, et al.
0

In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of message-passing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In this paper, we present a general approach based on linear constraint systems that naturally generates alternative explanations in an orderly and highly efficient manner. This approach is then applied to cost-based abduction problems as well as belief revision in Bayesian net works.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

03/27/2013

Strategies for Generating Micro Explanations for Bayesian Belief Networks

Bayesian Belief Networks have been largely overlooked by Expert Systems ...
10/04/2021

What is understandable in Bayesian network explanations?

Explaining predictions from Bayesian networks, for example to physicians...
02/27/2013

Belief Maintenance in Bayesian Networks

Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning u...
03/06/2013

A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification

The problems associated with scaling involve active and challenging rese...
03/27/2013

Implementing a Bayesian Scheme for Revising Belief Commitments

Our previous work on classifying complex ship images [1,2] has evolved i...
03/27/2013

Distributed Revision of Belief Commitment in Multi-Hypothesis Interpretations

This paper extends the applications of belief-networks to include the re...
06/03/2019

Parameterised Complexity of Abduction in Schaefer's Framework

Abductive reasoning is a non-monotonic formalism stemming from the work ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.