Actively Interacting with Experts: A Probabilistic Logic Approach

Machine learning approaches that utilize human experts combine domain experience with data to generate novel knowledge. Unfortunately, most methods either provide only a limited form of communication with the human expert and/or are overly reliant on the human expert to specify their knowledge upfront. Thus, the expert is unable to understand what the system could learn without their involvement. Allowing the learning algorithm to query the human expert in the most useful areas of the feature space takes full advantage of the data as well as the expert. We introduce active advice-seeking for relational domains. Relational logic allows for compact, but expressive interaction between the human expert and the learning algorithm. We demonstrate our algorithm empirically on several standard relational datasets.

[1]  Luc De Raedt,et al.  ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.

[2]  Jennifer Neville,et al.  Relational Active Learning for Joint Collective Classification Models , 2011, ICML.

[3]  Ben Taskar,et al.  Probabilistic Entity-Relationship Models, PRMs, and Plate Models , 2007 .

[4]  Pedro M. Domingos,et al.  Markov Logic: An Interface Layer for Artificial Intelligence , 2009, Markov Logic: An Interface Layer for Artificial Intelligence.

[5]  Burr Settles,et al.  Active Learning , 2012, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[6]  Jude W. Shavlik,et al.  Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another , 2005, ECML.

[7]  Jude W. Shavlik,et al.  Online Knowledge-Based Support Vector Machines , 2010, ECML/PKDD.

[8]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[9]  Diane J. Cook,et al.  Ask me better questions: active learning queries based on rule induction , 2011, KDD.

[10]  Huzefa Rangwala,et al.  FLIP: Active Learning for Relational Network Classification , 2014, ECML/PKDD.

[11]  Jude W. Shavlik,et al.  Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..

[12]  Kristian Kersting,et al.  Gradient-based boosting for statistical relational learning: The relational dependency network case , 2011, Machine Learning.

[13]  Sriraam Natarajan,et al.  Knowledge-Based Probabilistic Logic Learning , 2015, AAAI.

[14]  Alan Fern,et al.  Imitation Learning with Demonstrations and Shaping Rewards , 2014, AAAI.

[15]  Glenn Fung,et al.  Knowledge-Based Support Vector Machine Classifiers , 2002, NIPS.

[16]  Thomas G. Dietterich,et al.  Learning first-order probabilistic models with combining rules , 2005, Annals of Mathematics and Artificial Intelligence.

[17]  Sriraam Natarajan,et al.  Active Advice Seeking for Inverse Reinforcement Learning , 2015, AAAI.

[18]  Bin Wu,et al.  Exploiting Network Structure for Active Inference in Collective Classification , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[19]  Lise Getoor,et al.  Active Learning for Networked Data , 2010, ICML.

[20]  Kristian Kersting,et al.  Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine , 2015 .

[21]  Hendrik Blockeel,et al.  Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..

[22]  Sriraam Natarajan,et al.  Guiding Autonomous Agents to Better Behaviors through Human Advice , 2013, 2013 IEEE 13th International Conference on Data Mining.

[23]  Thomas Gärtner,et al.  Simpler knowledge-based support vector machines , 2006, ICML.

[24]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[25]  Luc De Raedt,et al.  Probabilistic inductive logic programming , 2004 .

[26]  Sofus A. Macskassy.  Using graph-based metrics with empirical risk minimization to speed up active learning on networked data , 2009, KDD.