Basic Independence Results for Maximum Entropy Reasoning Based on Relational Conditionals

15 pages•Published: October 19, 2017

Marco Wilhelm, Gabriele Kern-Isberner and Andreas Ecke

Abstract

Maximum entropy reasoning (ME-reasoning) based on relational conditionals combines both the capability of ME-distributions to express uncertain knowledge in a way that excellently fits to commonsense, and the great expressivity of an underlying first-order logic. The drawbacks of this approach are its high complexity which is generally paired with a costly domain size dependency, and its non-transparency due to the non-existent a priori independence assumptions as against in Bayesian networks. In this paper we present some independence results for ME-reasoning based on the aggregating semantics for relational conditionals that help to disentangle the composition of ME-distributions, and therefore, lead to a problem reduction and provide structural insights into ME-reasoning.

Keyphrases: aggregating semantics, independence results, principle of maximum entropy, relational probabilistic conditionals

In: Christoph Benzmüller, Christine Lisetti and Martin Theobald (editors). GCAI 2017. 3rd Global Conference on Artificial Intelligence, vol 50, pages 36-50.

Links:	https://easychair.org/publications/paper/jzdz
	https://doi.org/10.29007/w7b5

BibTeX entry

@inproceedings{GCAI2017:Basic_Independence_Results_Maximum,
  author    = {Marco Wilhelm and Gabriele Kern-Isberner and Andreas Ecke},
  title     = {Basic Independence Results for Maximum Entropy Reasoning Based on Relational Conditionals},
  booktitle = {GCAI 2017. 3rd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzmüller and Christine Lisetti and Martin Theobald},
  series    = {EPiC Series in Computing},
  volume    = {50},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/jzdz},
  doi       = {10.29007/w7b5},
  pages     = {36-50},
  year      = {2017}}

Download PDF Open PDF in browser