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Statistical Relational Artificial Intelligence

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Front. Robot. AI | doi: 10.3389/frobt.2018.00056

Human-Guided Learning for Probabilistic Logic Models

  • 1Georgia Institute of Technology, United States
  • 2The University of Texas at Dallas, United States
  • 3Indiana University Bloomington, United States

Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a ``mere labeler" in recent times.
We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice.
Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are then explicitly considered by an iterative learning algorithm at every update.
Our empirical evidence shows that human advice can effectively accelerate learning in noisy, structured domains where so far humans have been merely used as labelers or as designers of the (initial or final) structure of the model.

Keywords: Statistical Relational Learning, advice-giving, Knowledge-based learning, boosting, Feature Selection

Received: 18 Nov 2017; Accepted: 20 Apr 2018.

Edited by:

Shlomo Berkovsky, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia

Reviewed by:

Saturnino Luz, University of Edinburgh, United Kingdom
Elena Bellodi, University of Ferrara, Italy
Riccardo Zese, University of Ferrara, Italy  

Copyright: © 2018 Odom and Natarajan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Phillip Odom, Georgia Institute of Technology, Atlanta, United States, phodom@umail.iu.edu