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Basic Independence Results for Maximum Entropy Reasoning Based on Relational Conditionals

15 pagesPublished: October 19, 2017

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:
BibTeX entry
@inproceedings{GCAI2017:Basic_Independence_Results_for,
  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 Benzm\textbackslash{}"uller and Christine Lisetti and Martin Theobald},
  series    = {EPiC Series in Computing},
  volume    = {50},
  pages     = {36--50},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/jzdz},
  doi       = {10.29007/w7b5}}
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